MC Project

Google’s Illyes: Robots.txt Covers Crawling, Not AI Training

support@mycoding.id (Matt G. Southern) — Thu, 22 Oct 2026 11:58:04 +0700

Robots.txt, as the net norm RFC 9309 defines it, governs which URLs a crawler may request, not what happens to the satisfied afterward, Google’s Gary Illyes stated in a Search Central video published Wednesday. To keep one AI crawler out during allowing others, he said, you have to mark that crawler’s user-agent token in the file.

The video is the second event of The Non-Canonical Cabinet, anywhere Illyes and Cherry Prommawin of Google Search Relations obtain edge-case questions from the community. Illyes is additionally co-author, alongside Mozilla’s Martin Thomson, of an IETF Internet-Draft that would provision robots.txt a norm way to province use preferences. Prommawin recommended penning non-English regulation paths in percent-encoded form for crawlers another than Google’s.

What Illyes Said About AI Training

The episode’s inquiry was whether robots.txt can authorize AI crawling of a website and keep that identical data out of generative example training. According to Illyes, “So as defined by RFC 9309, robots.txt is an admission penchant protocol.” He went on to say robots.txt “doesn’t dictate what a scheme does alongside the data following it has already crawled it.” If you desire to entirely obstacle a particular AI crawler from accessing your complete website, Illyes stated, you need to “target their particular person delegate tokens explicitly in the file.”

The event illustrated a fictional directive (e.g., No-AI-Chicken-Training) which was shown on-screen and would be ignored by a parser. Google’s robots.txt specification page states that the four elements supported are User-Agent, Allow, Disallow, and Sitemap; invalid lines volition be ignored by Google.

As for RFC 9309 specifically (Section 2.2.4), it allows crawlers to decide how they’ll procedure a document exterior the range of this protocol. Therefore, whether or not an additional row accomplishes item depends on the crawler interpreting it.

What Google-Extended Controls

Google-Extended is a robots.txt token alongside no person delegate of its own. Google crawls under its existing person agents, and Google’s list of average crawlers says the token is “used in a authority capacity.” It manages whether satisfied Google crawls can be used to train forthcoming Gemini models and for grounding in Gemini Apps and Grounding alongside Google Search on Vertex AI. The leaf says it doesn’t power a site’s inclusion in Google Search and isn’t a ranking signal.

Google-Extended’s definition as a use authority comes from Google’s records for its own products, fairly than RFC 9309. The hosts didn’t conversation it on air, but the video’s clarification links to the identical list.

The Draft That Would Add A Usage Rule

The current type of the IETF outline Illyes co-wrote, dated Aug. 19, would update RFC 9309, if approved, alongside a Content-Usage regulation for robots.txt and a matching HTTP header. Neither the event nor its clarification mentions the draft.

Under the draft, Allow and Disallow would continue to govern which URLs can be crawled. Content-Usage would afterward define preferences as to how crawled satisfied have to be used. Those preferences would lone connect to paths anywhere a crawler has approval to fetch content. A Content-Usage site of “train-ai=n” is added to the draft’s example robots.txt file, which allows crawling.

The outline is motionless a Working Group Document. At present, its IETF Datatracker admission shows that the Working Group has not yet submitted the document for assessment by the IETF’s Steering Group (IESG). The Working Group’s planned date for sending it to the IESG was Aug. 2026. The ‘train-ai’ Label referenced complete appears in a distinct Vocabulary Draft, dated Sept. 14. That document states that it reflects no agreement inside the Working Group.

Percent-Encode Non-English Paths

Cherry Prommawin’s direction for paths alongside non-English characters or emoji was that “you should use the encoded type in your robots.txt rules.” Google’s parser handles raw characters by encoding them, she said, but “other or older crawlers on the web power get confused.”

The on-screen example tagged a raw emoji way “Works for Google, power interrupt others” and its percent-encoded form “The Safe, Standard Way.” Google’s spec leaf says its parser treats raw UTF-8 and percent-encoded paths the identical way, and RFC 9309’s grammar allows UTF-8 characters in paths.

Why This Matters

The method you would need in robots.txt to bounds AI use of your satisfied during keeping your location apparent in Google Search depends on what bot or merchandise you are operating with. Disallow statements may forestall particular AI crawlers from accessing particular webpages. For Gemini, the documented authority of Google is Google-Extended and includes the two training and grounding. There currently is no norm way of expressing a training penchant autonomous of the bots allowed to crawl the webpage; one exists lone as a draft.

Websites that use URLs alongside languages another than English volition desire to encode their robots.txt paths using percent encoding. Percent encoding is safer because, although Google treats the two raw and encoded versions of the identical robots.txt regulation way equally, another crawlers could possibly construe raw characters differently.

Looking Ahead

According to the video description, the next Non-Canonical Cabinet event volition shield the 404 position code. The current Content-Usage outline is set to expire on Feb. 20, 2027, unless a new revision replaces it sooner.

Featured Image: Alicia97/Shutterstock

Google wordmark: Source: Google.

Category News AI Search

Elon Musk says training Tesla's Optimus robot is like raising a toddler

support@mycoding.id (Tom Carter) — Thu, 22 Oct 2026 11:33:17 +0700

Elon Musk compared training Optimus to raising a toddler. Riccardo De Luca/Anadolu via Getty Images

Tesla is sending its Optimus humanoid robot rear to school.

Speaking on the company's earnings call on Wednesday, CEO Elon Musk compared training the EV maker's humanoid robot to raising a toddler.

Musk stated that during Tesla's autonomous driving models can study from data collected by its enormous fleet of vehicles, its Optimus robot has to study by observing group do tasks and practicing them itself, a procedure akin to how humans learn.

"You can see this whenever you have a toddler, and they initially are kind of wobbly, and they don't cognize how to do things, and eventually, they study to rub their own ass, which is a big milestone," Musk told investors.

"They study to peruse and do all sorts of things, and they study this by playing. And we really need Optimus to perform at measure in command to have it be really fine at tasks in the genuine world," Musk said.

Musk stated Tesla expects to commencement delivering Optimus to consumers by the end of 2027. Bloomberg/Getty Images

The Tesla CEO added that the business was construction out an "Optimus Academy" anywhere prototypes can custom a broad range of tasks and create training data to instruct forthcoming versions of Optimus.

"For Optimus to get the training data we need, we really need to create a lot of Optimi. So we need to make, I think, on the command of 10,000 or 20,000 bots and afterward have them do self-play," stated Musk.

Tesla stated on Wednesday that it had begun production of Optimus at its Fremont factory, alongside Musk saying the business expects to commencement delivering Optimus to customers by the end of next year.

The near-trillionaire cautioned that manufacturing would be "somewhat bumpy" and that betterment of the humanoid robot motionless has several way to go.

He listed the challenges of designing durable yet receptive robotic hands, as fine as AI capable of handling open-ended tasks, among the chief hurdles Tesla faces.

Tesla is not solitary in environment up robot academies to provision training data for humanoid robots.

Unlike ample tongue models akin ChatGPT, which were trained on the huge corpus of content data accessible on the internet, robotics faces a shortage of real-world training data to instruct humanoids to navigate the bodily world.

Other startups, specified as Texas-based Apptronik, have additionally set up "data factories" anywhere robots burden conveyor belts and kind objects to create training data to combat the data shortage.

SEO at scale: How to manage SEO on a large website

support@mycoding.id (Putri Silvia) — Thu, 22 Oct 2026 11:11:00 +0700

A location alongside 40,000 URLs and a two-person SEO squad can't be managed leaf by page. You activity alongside templates and rules that authority thousands of pages at once.

In this guide, you’ll study how to prioritize SEO activity on a ample website, create changes safely, and place achievement problems throughout distinct sections of your site.

What SEO at measure means

SEO at measure is the procedure of managing a website’s integrated hunt visibility whenever it has too many pages to optimize individually.

Most ample sites autumn into one of three types:

Ecommerce: A retailer specified as IKEA can have thousands of merchandise pages, alongside alongside category pages that assistance shoppers browse those products
Multi-location services: A business specified as Mr. Rooter Plumbing has pages for distinct locations, alongside idiosyncratic locations additionally covering the plumbing services they offer
Software as a assistance (SaaS): A application business specified as Shopify can have records covering many features, tasks, and troubleshooting questions

The SEO basics motionless apply. Search engines need to discover your crucial pages, comprehend their content, and decide whenever to display them in hunt results. But at scale, the difficulty is applying those fundamentals consistently throughout thousands of pages.

I’ll concentration on managing SEO issues and achievement throughout a ample website in this guide. If you desire assistance producing satisfied at scale, see our SEO satisfied guide.

How to oversee SEO at scale

Managing SEO at measure comes downward to five steps: arrange your pages by type, prioritize the types that matter most, fix the problems they share, rotate changes out safely, and detect how all category performs.

The final four steps repeat. Large sites keep adding, changing, and removing pages, so what you see during monitoring sets your next priorities. Our enterprise SEO case studies display how another teams run this process.

Follow these steps to create SEO activity manageable throughout thousands or equal millions of pages:

1. Organize SEO activity by leaf type

On a ample site, the template is your component of work. If 4,000 category pages portion one template, changing that template changes all 4,000 pages. Change one leaf and you've changed one.

A leaf category is a collection of pages that serves the identical purpose, specified as merchandise pages, category pages, location pages, or assistance articles. Pages of the identical category normally portion a template or prosecute the identical rules, so grouping your URLs by leaf category is how you discover the templates value operating on.

The exact page-type groups volition change by website.

Home Depot's sitemap shows multiple leaf types flank by side: merchandise categories specified as "Electrical" and "Batteries," and project pages specified as "Bathroom Ideas" and "Kitchen Ideas."

Mr. Rooter has location pages, assistance pages, and pages that merge a location alongside a particular service.

Shopify's Help Center is a sole leaf type, assistance articles, organized into topics specified as apps, products, payments, and item of sale.

To discover these leaf types on your own site, commencement by looking at how your URLs are organized. You can use Semrush’s Site Audit, which can crawl up to 100,000 pages in a sole audit and up to one myriad pages per duration on the Business tier of the SEO Toolkit.

Create a Site Audit project for your domain, configure the audit, and afterward click “Start audit.”

Go to the “Crawled Pages” study and click “Site Structure.” This shows crawled URLs organized by subdomain and subfolder, alongside alongside the figure of URLs in all section.

Look for folders that equivalent to your chief leaf types. For example:

/products/ may merge merchandise pages
/collections/ may merge category pages
/locations/ may merge location pages
/blog/ may merge blog posts

Use these sections to create your chief page-type groups.

If your URL construction doesn’t plainly distinct leaf types, inspect how pages are classified in your content administration system (CMS). Your CMS may already collection pages as products, categories, articles, locations, or another leaf types.

2. Prioritize leaf types by endeavor impact

Rank all leaf category by how immediately it contributes to revenue, leads, or another center endeavor outcome.

What counts as elevated effect depends on your endeavor category and how it makes money. For example:

An ecommerce site akin IKEA may treat merchandise pages as elevated effect since they can immediately guide to purchases, during ideas and inspiration articles may be lesser effect if they chiefly assistance research
A multi-location assistance site akin Mr. Rooter may treat assistance pages as elevated effect since they can create calls or bookings, during blog posts may be lesser effect if they chiefly answer informational questions
A SaaS location akin Shopify may treat characteristic pages as elevated effect since they can power signups or demo requests, during assistance center articles may be lesser effect if they chiefly assistance existing customers

In general, leaf types that sit nearer to a purchase, booking, or qualified guide volition frequently deserve additional notice than pages that chiefly assistance investigation or provision information.

So a leaf category that gets 40,000 visits but generates few conversions may deserve small notice than one that gets 3,000 visits and consistently drives revenue or leads.

Use your analytics, ecommerce, client association administration (CRM), or another endeavor data to see which leaf types create revenue, leads, bookings, or another precious conversions. Then allocate all leaf category a precedence specified as high, medium, or low according to that contribution.

If your analytics setup can’t distinct achievement by leaf type, use Semrush’s Organic Rankings as a fallback. Enter the complete subfolder URL for a leaf category (e.g. "example.com/products/") to see its estimated integrated traffic and ranking keyword count. Repeat this for your chief leaf types to difference their integrated hunt performance.

Once you’ve prioritized the leaf types by endeavor impact, use fixability to decide which of the high-impact groups to activity on first.

A leaf category is easier to enhance at measure whenever many affected URLs portion a template, rule, or component that you can fix once.

A leaf category is harder to act on whenever the problems change from leaf to leaf or fixing them would necessitate a important redesign, migration, or ample amount of manual work.

You can use Semrush’s Site Audit to inspect whether the identical matter affects many URLs in a high-impact leaf type. Open the “Issues” study and click an matter to see all the URLs it affects.

Then use the “Advanced filters” choice to act the subfolder for one of your high-impact leaf types. For example, act “/products/” for merchandise pages. This lets you see whether the identical matter appears throughout many URLs in that collection fairly than on lone a few idiosyncratic pages.

At the end of this step, all leaf category should have a precedence from elevated to low, alongside the strongest blend of endeavor effect and fixability at the top.

3. Fix recurring SEO issues at scale

Fix recurring SEO issues in your highest-priority leaf types by identifying and correcting the shared cause.

Common issues you can location throughout groups of pages include:

Site taxonomy and URL structure

Site taxonomy is how you collection pages into categories and subcategories, during URL structure is how those pages are represented in their web addresses.

For example, IKEA organizes its merchandise categories inside a apparent ranking and uses accordant URL patterns for distinct leaf types.

On a ample site, taxonomy and URL decisions can power complete groups of pages. A poorly planned category or URL form can create overlapping pages, unnecessary levels in the hierarchy, or multiple URLs serving basically the identical purpose.

For all high-priority leaf type, assessment how its categories and subcategories are organized in your navigation or CMS. Then appearance for repeated URL patterns throughout that section. Check for problems specified as:

Categories or subcategories that substantially overlap
Pages grouped under the incorrect genitor category
Category levels that add small helpful distinction

Suppose a location has the two “/category/subcategory/” and “/subcategory/,” and the two URLs portray the identical category. Instead of maintaining both, the location could choose one structure, move the applicable items or pages under it, update internal links, and redirect the redundant URL to the one it keeps.

Depending on the exact matter you find, you may need to:

Combine overlapping categories: Keep one category whenever two categories assist basically the identical purpose
Move pages to a better-fitting category: Place pages under the category that finest matches what they contain
Remove unnecessary category levels: Simplify parts of the ranking that don’t add a helpful distinction
Use accordant URL patterns: Follow the identical URL construction for pages of the identical type

If the identical URL-structure issue affects many pages of the identical type, alter the URL form in your CMS fairly than editing all URL individually. Apply redirects using the identical regulation so affected URLs consistently item to the new structure.

Crawling and indexation

Crawling is how hunt engines detect and fetch your pages, during indexation is how eligible pages are added to a hunt engine’s indicator so they can appear in hunt results.

On a ample site, you need to oversee crawl budget so hunt engines expend additional of their crawling resources on crucial pages. Thousands of unnecessary filters, parameters, or copy URLs can consume crawl prosperity and, if indexed, contribute to indicator bloat.

For example, an ecommerce category leaf may let shoppers display products by size, color, brand, price, and availability. Different display combinations can create many URLs, equal although many of those URLs don’t need to appear in hunt results.

Check your high-priority leaf types for:

Access errors: 4xx or 5xx responses that forestall crawlers from reaching crucial pages
Unreachable sitemap URLs: Pages listed in your sitemap that crawlers cannot access
Crawl blocks: Important URLs blocked by robots.txt rules
Indexing blocks: Important pages alongside a noindex directive that prevents them from appearing in hunt results
Unnecessary URL variations: Filters or parameters that create ample numbers of crawlable URLs
Pagination problems: Paginated pages without distinctive URLs or crawlable links to afterward pages

You can acknowledge these issues in Semrush’s Site Audit. In the “Overview” tab, scroll to the “Thematic Reports” division and under “Crawlability,” click “View details.”

Review “HTTP Status Code” for pages returning 4xx or 5xx errors. Then inspect “Sitemap vs. Crawled Pages” to see whether Site Audit could attain the URLs listed in your sitemap.

Next, go to the “Issues” tab and hunt “block” to discover crucial URLs affected by crawl or indexing blocks.

Then go to the “Crawled Pages” tab and hunt for URL patterns created by filters or parameters, specified as “?color=” in the URL. This can disclose ample groups of URLs created by the identical display or parameter.

On larger or additional complex sites, Semrush’s Enterprise Site Intelligence includes Bot Analytics, which uses server log data to display which URLs search-engine bots petition and how often. This can verify whether bots are reaching URLs from your high-priority leaf types or often crawling unnecessary URL patterns.

Depending on the issues you find, you may need to:

Update crawl rules: Remove or alter a robots.txt regulation that blocks crucial URLs
Fix admission errors: Resolve leaf or server errors that halt crawlers from reaching crucial URLs
Control display URLs: Keep helpful display combinations indexable and forestall low-value combinations from being crawled
Fix pagination: Give paginated pages distinctive URLs and nexus them alongside crawlable links
Fix noindex rules: Remove noindex directives from pages that should appear in hunt results

If the identical crawl matter affects many URLs, fix the regulation creating or exposing those URLs fairly than handling them one by one. For example, alter the nexus markup that generates crawlable display URLs, or update the applicable robots.txt regulation whenever you desire to forestall crawlers from accessing a URL pattern.

Duplicate and near-duplicate content

Duplicate and near-duplicate content occurs whenever distinct URLs merge the identical or extremely akin chief content.

On ample websites, this normally happens since the location creates multiple URLs from the identical underlying satisfied or leaf template.

Common causes include:

Product variants: Different versions of the identical merchandise have distinct URLs alongside mostly akin content
Filters and sorting options: Different URL combinations display many of the identical products
Similar categories: Separate category pages merge nearly the identical products and copy

For example, IKEA gives all type of a merchandise its own URL. This is the yellow version:

A distinct type of the identical merchandise is accessible at another URL:

Much of the leaf satisfied is the identical throughout the two URLs, during particulars specified as the color, price, or matter differ. Whether those type URLs should remain indexable depends on hunt demand. If group hunt for a particular variant, specified as “yellow section chair,” keeping that leaf indexable may create sense. If a type has small distinct hunt demand, you can use a canonical tag to signify the preferred merchandise URL.

When multiple near-duplicate URLs remain indexable, hunt engines motionless need to decide which type is most applicable for a query. They may display a distinct URL from the one you expect.

Duplicate URLs can additionally discarded crawl resources and divided ranking signals whenever links item to distinct versions alternatively of one preferred URL.

Start by finding copy and near-duplicate pages inside the high-priority leaf types you identified earlier.

In Semrush’s Site Audit, go to the “Issues” tab and hunt “duplicate content” to see pages alongside at smallest 85% identical content.

Click the matter to see the affected URLs, afterward display the catalog to the leaf category you’re reviewing, specified as merchandise or category pages.

Once you’ve identified the affected pages, collection them by the form causing the duplication, specified as merchandise variants, filters, or akin categories, and decide how all form have to be handled.

Depending on the exact matter you find, you may need to:

Redirect or merge unnecessary duplicates: Keep one type whenever multiple pages assist basically the identical purpose
Add or accurate canonical tags: Tell hunt engines which URL you favor as the chief type whenever akin pages need to remain available
Differentiate pages that need to position separately: Update the shared template or CMS sectors so pages can contain distinct data specified as merchandise attributes, category details, or location-specific content

If copy URLs are being created throughout a leaf type, fix the shared origin fairly than correcting pages one by one. For example, update the canonical regulation in the applicable template or alter the URL-generation regulation that creates the copy versions.

Multilingual SEO and hreflang

Hreflang helps hunt engines comprehend which type of a leaf is intended for users in a particular tongue or region.

For example, a website may have one type of a merchandise leaf in French for users based in France and another in German for users based in Germany. Hreflang tells hunt engines which type is meant for all audience.

On ample sites, one incorrect hreflang setup can power many localized pages.

Check your high-priority leaf types for:

Missing come back links: One localized leaf points to another, but the another leaf does not item back
Incorrect tongue or area codes: Hreflang uses a code that hunt engines don’t recognize, specified as “en-UK” alternatively of the valid “en-GB”
Invalid destination URLs: Hreflang points to a leaf that redirects, returns an error, or cannot be indexed

You can discover these problems in Semrush’s Site Audit. In the “Overview” tab, scroll to the “Thematic Reports” section, and under “International SEO,” click “View details.”

Review hreflang issues specified as incorrect hreflang codes and another implementation errors. Click “# issues” to see the affected pages.

Then open "Hreflang conflicts inside leaf origin code" to see all affected page, its hreflang values, and the category of issue.

After identifying the affected pages, accurate the hreflang tags by adding missing come back links, fixing incorrect tongue or area codes, and replacing URLs that redirect or cannot be indexed. If the identical error appears throughout many localized pages, update the shared regulation that creates those hreflang tags fairly than editing all leaf separately.

Internal linking

Internal links assistance users and hunt engines discover connected pages and move through your site.

On ample sites, crucial pages can rotate into orphaned, which method no inner links item to them. Or they may sit additional than three clicks from the homepage, which indicates elevated crawl depth.

You can discover inner nexus problems in Semrush’s Site Audit. In the “Overview” tab, scroll to the “Thematic Reports” section, and under “Internal Linking,” click “View details.”

Review the crawl-depth diagram to see how many pages sit multiple clicks from the homepage. Then inspect the errors, warnings, and notices for issues specified as orphaned sitemap pages, damaged inner links, and pages alongside lone one inner link. Click “# issues” to see the affected URLs.

For orphaned pages that should remain on the site, add links from applicable pages that users and hunt engines can already reach. For deeply buried pages, create shorter paths from category, hub, or another notable pages.

If many pages of the identical category are affected, update the navigation or related-content division for that leaf category so you can add links throughout the collection alternatively of leaf by page.

JavaScript rendering

JavaScript rendering is the procedure of operating JavaScript code so the final satisfied and links on a leaf can appear.

On a ample site, a rendering issue in a shared template can power all leaf that uses it.

For example, if a template relies on JavaScript to add inner links and those links don’t render correctly, hunt engines may have difficulty finding the pages they guide to. Google can procedure JavaScript, but JavaScript-generated satisfied and links motionless need to render in a format Google can admission and crawl.

You can inspect whether JavaScript changes elements specified as content, links, canonical tags, or meta robots directives alongside Semrush’s Site Audit. When JavaScript rendering is enabled, its “JS Impact” study shows what changes before and following JavaScript renders.

If the identical rendering issue appears throughout many pages, fix the shared template or code causing it. Then rerun Site Audit to verify the affected satisfied or links now render correctly.

Structured data

Structured data is standardized code that tells hunt engines what particular data on a leaf means, specified as a product’s price, availability, or rating.

It can create eligible pages appear as affluent results that display several of those particulars immediately in Google.

Structured data errors can dispersed throughout thousands of pages whenever the markup is generated from a shared template or data source.

For example, an ecommerce location may use its merchandise template to add Product organized data to all merchandise page. If the template pulls the incorrect cost or preparedness field, the identical error can appear throughout the complete leaf type.

Look for recurring problems specified as:

Missing properties: The identical required site is absent throughout many pages
Invalid markup: The identical organized data error appears often throughout one leaf type
Incorrect values: Prices, availability, or another values don’t equivalent the data shown on the page

You can discover organized data problems in Semrush’s Site Audit. In the “Overview” tab, scroll to the “Thematic Reports” division and under “Markup,” click “View details.”

Review the “Structured Data Items” division to see which markup types are valid or invalid. Open an invalid item to assessment the affected pages and the sectors that need attention.

Look for the identical error throughout URLs that portion a leaf type. For example, if hundreds of "/product/" URLs are missing the identical property, the issue apt comes from the merchandise template or the data feeding that property.

Fix the shared origin according to what you find.

If a asset is missing, add it to the template that generates the markup
If a value is wrong, accurate the CMS or product-catalog site supplying that value, or fix how that site maps to the organized data

Rerun Site Audit afterward to verify the error has been resolved throughout the affected pages.

4. Roll out changes safely

Roll out ample SEO changes in stages so you can verify that they activity as intended before applying them throughout the site.

A faulty large-scale alter can logic rankings and integrated traffic to autumn throughout a ample part of the site. Making multiple important changes at formerly can additionally create it harder to acknowledge what caused the problem.

Use these safeguards whenever implementing changes throughout many pages:

Set a pre-rollout baseline: Record integrated traffic and indexed-page counts for all affected leaf type, and set up a Position Tracking run alongside tags for the keywords tied to all leaf type

Prepare a rollback option: Keep the former template type so you can reconstruct it quickly if the rollout causes problems
Group implementation by template: If a developer needs to create the change, create one ticket for the shared template alternatively of listing all affected URL
Select an owner for all template: Decide who signs off on changes to all shared template, and keep a abbreviated checklist of the checks you run before all release
Test and crawl the alter in staging: Apply the update to a delegate set of pages in a staging environment, afterward crawl the staging location alongside Site Audit to inspect for specialized problems before launch
Expand the rollout in batches: Once the test passes QA, use the alter to additional groups of pages fairly than updating the complete location at once. This makes it easier to isolate a issue if one appears.

After all rollout, rerun Site Audit to inspect whether the alter introduced new specialized issues. Go to the “Compare Crawls” study to difference the latest crawl alongside the former one and see which issues are new or fixed.

For larger sites, Semrush's Enterprise Site Intelligence monitors specialized health continuously and flags new issues as they appear, so your squad doesn't have to keep re-checking the location by hand.

Site migrations

A rebuild is the riskiest alter a ample location can make. Rankings frequently autumn and can obtain months to recover, and whenever everything changes at once, you can't inform which alter caused the drop.

Restructure one division at a period whenever you can. Migrate one leaf category or subfolder, inspect its crawlability, indexation, redirects, and integrated traffic against your baseline, and fix any problems before you move on.

Whether you move one division or the entire site, these aren't optional:

Crawl the old location first: Save all current URL and its crawl data so you can inspect the new location against it following launch
Build a complete redirect map: Map all URL that changes to its new destination before item moves
Avoid redirect chains: Point old URLs direct to their final destination

After launch, crawl the new location and difference it alongside the old crawl to discover missing pages and damaged redirects. Our website immigration checklist covers the complete process.

5. Monitor achievement by segment

Once your changes are live, detect the high-priority leaf types you worked on to see anywhere SEO performance improves or declines.

Tracking those leaf types separately helps you place changes that sitewide averages can hide. For example, general integrated traffic may remain flat equal if merchandise pages are losing traffic since growth in blog traffic offsets the decline.

For all high-priority leaf type, track metrics that display the two hunt achievement and endeavor impact:

Indexed pages: Track how many crucial URLs in all leaf category are included in Google’s index
Organic traffic: See whether visits from hunt are expanding or decreasing
Organic conversions: Track revenue, leads, bookings, or another precious act generated by the leaf type
Search visibility and rankings: Monitor whether the leaf category is gaining or losing visibility for its mark keywords

In your analytics platform, collection integrated landing pages using the identical URL patterns or CMS labels you established earlier. Compare all group’s integrated visits and conversions alongside its pre-rollout baseline, accounting for seasonal changes.

Use Google Search Console to detect which pages from all leaf category are indexed. Create distinct XML sitemaps for important leaf types, specified as product, category, or location pages, and present them under “Sitemaps.”

Then, go to “Pages” to see your leaf indexing report. Use the sitemap display to choose the sitemap for a leaf type.

Review how many URLs are indexed or not indexed, and inspect the reasons Google gives for pages it hasn’t indexed.

You can use Semrush’s Position Tracking to detect anywhere your location ranks for the keywords connected alongside all high-priority leaf type.

Add those keywords whenever you set up the campaign, afterward allocate tags specified as “Product pages” or “Category pages” to collection them.

You can additionally add tags to keywords afterward from the “Rankings Overview” report.

Then open the “Tags” study to difference visibility, portion of voice, estimated traffic, and average stance throughout those groups.

Check which URLs position for those keywords before attributing a visibility alter to a leaf type.

If the keyword collection tied to one leaf category loses visibility during the others remain stable, examine that leaf category first. Check its crawl and indexation health to see whether a specialized matter could be contributing to the decline.

Monitor AI hunt visibility

Monitor AI hunt visibility to create certain large-scale SEO changes aren’t reducing how frequently your brand is mentioned or your pages are cited in AI-generated answers.

Changes to crawlability, inner linking, or page structure can power how effortlessly AI crawlers detect and admission your pages, which can power their chances of being cited.

After any robots.txt, CDN, or firewall change, open Site Audit's "Crawled Pages" study and use the "Blocked AI hunt bots" display to verify AI crawlers can motionless attain your precedence leaf types.

Track center signals:

Mentions: Identify the prompts and topics anywhere your brand appears
Citations: Track how frequently your location is cited and which pages AI platforms use as sources

Pay particular notice to prompts connected to the high-priority leaf types you changed and the pages cited for those prompts. If mentions or citations autumn following a rollout, inspect whether the autumn involves those leaf types.

You can detect these signals alongside Semrush’s AI Visibility Toolkit. Add your domain to see your AI Visibility Score, mentions, citations, cited pages, and how mentions are distributed throughout supported AI platforms.

In Prompt Tracking, you can add particular prompts to detect regular throughout ChatGPT, Google AI Mode, and Gemini. The study shows how your AI visibility, mentions, and average stance alter complete time.

For tracking AI visibility throughout multiple brands or markets, you can use Semrush’s Enterprise AIO. It supports unlimited immediate tracking throughout platforms including ChatGPT, Gemini, Claude, and Perplexity.

Automate repeatable SEO tasks

Automate repeatable SEO tasks that prosecute apparent rules and recur throughout ample parts of your site.

You can automate:

Monitoring and alerts: Schedule specialized checks and get notified whenever new issues appear
Performance tracking: Automatically collect ranking, traffic, and AI visibility data alternatively of checking all leaf category manually
Internal linking analysis: Automatically scan ample groups of pages for orphaned pages, feeble inner linking, or another recurring linking gaps
Reporting: Generate recurring reports for distinct websites, markets, or endeavor units

SEO tools can grip much of this recurring activity formerly you configure what to monitor. For example, Site Audit can run scheduled crawls and Position Tracking can detect hunt rankings and AI visibility complete time.

Keep individual assessment for changes that can power ample parts of the site, particularly template changes and item that creates or removes pages in bulk.

Programmatic SEO is one example of automation that motionless needs individual review. It uses templates and data to create many connected pages, so assessment those pages before publishing them at scale. Google may treat large-scale leaf creation as spam whenever its chief intent is manipulating hunt rankings fairly than assisting users.

Manage SEO at measure as an ongoing process

Large sites never halt changing, so this activity doesn't finish. Keep operating at the page-type level. Decide which types matter, fix what they share, rotate changes out in batches, and let all type's results inform you what to fix next.

To start, open Site Audit's "Crawled Pages" study and collection your URLs by leaf type.

Frequently asked questions

What counts as a ample website for SEO?

There's no worldwide cutoff. Google's crawl prosperity guide is written for sites alongside 1 million+ distinctive pages that alter weekly, or 10,000+ distinctive pages that alter daily, and Google says those aren't exact thresholds.

A website may need SEO-at-scale processes before if reviewing and optimizing URLs one by one is no longer practical.

At the elevated end, it overlaps alongside enterprise SEO, which brings its own challenges about teams, approvals, and tooling.

What are the biggest challenges of SEO at scale?

The biggest challenges of SEO at measure are deciding what to prioritize, finding issues that power many pages, rolling out changes safely, and tracking achievement throughout ample groups of URLs.

The larger the site, the additional crucial it becomes to activity on groups of akin pages fairly than treating all URL as a distinct SEO task.

Is programmatic SEO the identical as SEO at scale?

No, programmatic SEO is not the identical as SEO at scale.

Programmatic SEO uses automation, templates, and data to create ample numbers of pages targeting connected hunt queries. SEO at measure is broader and covers how you oversee SEO throughout a ample or complex website. Programmatic SEO can be one part of that process.

How do you evade copy satisfied on a ample site?

You can evade duplicate-content problems on a ample location by limiting unnecessary URL variations and making it apparent which type of akin pages hunt engines should treat as the chief one.

Use redirects whenever copy URLs are no longer needed, canonical tags whenever akin URLs must remain accessible, and carefully oversee filters, parameters, and local versions that can create copy URLs.

How frequently should you audit a ample website?

You should audit a ample website at smallest formerly a week, and run additional audits following important changes specified as migrations, template updates, or ample satisfied releases.

The correct frequence depends on how frequently the location changes. A frequently updated location may need regular specialized crawls, during a slower-moving location may lone need weekly crawls.

How do you track SEO achievement throughout thousands of pages?

Track SEO achievement throughout thousands of pages by grouping akin pages and measuring all collection separately alternatively of reviewing all URL individually.

For all group, detect integrated traffic, conversions, hunt visibility, and AI hunt visibility. This helps you see which parts of the location are improving, declining, or need notice next.

Hugging Face is launching a new AI model that lets scientists 'chat with DNA'

support@mycoding.id (Kelsey Vlamis) — Thu, 22 Oct 2026 11:00:01 +0700

Hugging Face released a new AI example that can analyze and create DNA sequences. Samuel Boivin/NurPhoto via Getty Images

Hugging Face is releasing an AI example that could create it easier for scientists to study DNA and create new familial treatments, the business announced Thursday.

Carbon-X is a "first of its kind" example that combines DNA inspection and generation alongside plain language, allowing researchers to ask questions and basically "chat alongside DNA," according to Lewis Tunstall, a elder investigation researcher at Hugging Face.

Hugging Face, which Nvidia accepted to acquire for nearly $13 milliard in September, is a phase for sharing and evolving AI models and additionally releases its own. The business worked alongside investigation labs in Italy and China on Carbon-X, which is an open-source model. Hugging Face built Carbon-X by training an existing tongue model, Qwen3-14B, on genomic sequences and natural language. It was trained on DNA sequences from humans, another mammals, plants, and fungi.

As an example of how Carbon-X can be used, Tunstall stated a investigator could provision a DNA sequence and ask whether a particular mutation could addition a person's hazard of disease. The example would afterward recommendation a prediction and explanation. It can additionally create new DNA sequences, which could assistance researchers create new cistron therapies.

Tunstall stated the business was partially motivated to build Carbon-X to forestall existence discipline researchers from becoming reliant on the important AI labs, noting how AI has disrupted mathematics.

"If you desire to really shove the frontiers of mathematics, you apt have to be at one of these AI labs akin OpenAI or Anthropic, since they're the ones who have the finest math models," he said, suggesting a akin circumstance is apt to perform out in another sectors of science.

"We wanted to provision the open investigation community alongside tools that don't create them completely reliant on several external sole provider," he added.

Researchers can run Carbon-X on their own computers, avoiding the need to portion delicate tolerant data alongside external AI companies. He additionally stated that several of the misalignment behavior observed in larger models, specified as the OpenAI agents that famously hacked Hugging Face in July, has not yet been observed in smaller, additional specialized models.

Tunstall stated generalist chatbots additionally sometimes refuse to answer lawful existence discipline questions from researchers since they error them for attempts to "design a bioweapon." He stated viruses were explicitly excluded from Carbon-X's training data in command to evade misuse of the model.

While Carbon-X is apt to be used by researchers, Tunstall stated the example represents a small stage toward a forthcoming anywhere AI can assistance provision additional personalized medicinal care.

"I think the holy grail is, in general, this idea of personalized medication anywhere we're capable to, as individuals, obtain our own tolerant records, our own genomes, and use this to ultimately discover or equal perchance scheme our own treatments," he said.

Trump says Google's CEO rejected his plan to change the Atlantic's name to the 'Trump Ocean'

support@mycoding.id (Georgia Hennessy) — Thu, 22 Oct 2026 10:55:40 +0700

After changing the names of the Gulf of Mexico and Lake Ontario, Trump has set his sights on a bigger build of water. Anna Moneymaker/Getty Images

President Donald Trump's scheme to rename the Atlantic Ocean hit choppy waters whenever he took it to Google.

At a Wednesday rally in South Carolina, Trump stated he considered putting his name or "America" on among the two oceans that flank the US: the Atlantic and the Pacific.

"I’m not going to put either Trump or America on any another sea but these two," he told the crowd.

Then, he said, he called Google to ask concerning making the alter on Google Maps.

The tech elephantine had earlier changed its US labels for the Gulf of Mexico and Lake Ontario to the Gulf of America and Lake America following administrator orders from the president.

"I said to a brilliant guy, Sundar. He heads all of Google," Trump said, referring to CEO Sundar Pichai. He additionally praised Google cofounder Sergey Brin, calling the two men "totally brilliant guys," before joking that they were "still rattled from the Gulf."

Trump recounted putting his idea immediately to Pichai: "Sundar, I’m thinking of the Atlantic Ocean. Can you grip it?"

"No, no, no. This period I’m not doing it," Trump quoted Pichai as replying. "Oceans are different, sir. They shield really a lot of countries. Please don't ask us to do that."

"So I'll likely pass," Trump told the crowd. "Should I continue it?" He afterward answered his own question: "I think let's keep it the Atlantic Ocean."

Google did not immediately react to a petition for comment.

It was not Trump's first sea pitch. At a Tennessee rally in first October, he floated "Trump Ocean" or "Ocean of the Americas" for the Atlantic or Pacific.

He has additionally mused concerning renaming a river, calling New Mexico "New America," and rechristening the Strait of Hormuz the "Trump Strait."

While those ideas remain proposals, Google Maps did clasp "Gulf of America" and "Lake America" for US users following the names changed in national records. Users in Mexico and Canada motionless see the first names for the gulf and lake, respectively. Users in the remainder of the earth see the first names, followed by Trump's new names in brackets.

The changes have created a dilemma for group who create maps for an global audience. "The matter of global acceptance is additionally a factor, as several nations may not obtain it," Joseph Wright, CEO of Replogle Globes, told Business Insider's Joshua Nelken-Zitser following the lake order.

"We market globes worldwide."

For now, Trump's Atlantic idea appears to have sunk before it reached Google Maps.

Elon Musk wants to give every American kid a piece of SpaceX. History shows what happens when millions become shareholders overnight.

support@mycoding.id (Tristin Hoffman) — Thu, 22 Oct 2026 10:49:01 +0700

Elon Musk says he'll provision all kid in the US a part of his area company, SpaceX. Kent NISHIMURA / AFP via Getty Images

Christmas came early: Elon Musk wants American children to own inventory in his area company, SpaceX, next year.

Musk wrote on X about 1 a.m. Monday that he planned to create the contribution next twelvemonth — "the archetypal of many," he said. SpaceX went public this summer.

Giving one portion to all of the estimated 72 myriad US children would disbursal approximately $10.7 milliard at SpaceX's decision cost of $147.92 on Friday. Musk's 4.77 milliard SpaceX shares were value concerning $706 milliard at that price, so the current would decrease his holdings by concerning 1.5%.

President Donald Trump stated on Truth Social on Monday that the shares would be transferred through Trump Accounts, tax-advantaged funding accounts for eligible American kids launched this summer. Under impermanent Treasury rules, donated shares mostly cannot be sold for five years.

"This is exactly what was envisioned whenever we created TRUMP ACCOUNTS," Trump said. "Bringing together the GREAT PRIVATE SECTOR, Elon and so many more, to allocate in our children."

The White House stated nearly 70 myriad Trump Accounts have been created. Numerous entities have accepted to contribute, including a $6.25 milliard contribution from billionaire brace Michael and Susan Dell for children in lower-income zip codes. In July, SpaceX President Gwynne Shotwell and her hubby pledged individual SpaceX shares value at smallest $321 million, prioritizing lower-income communities.

The White House, Elon Musk, and SpaceX did not immediately react to a petition for comment from Business Insider.

Musk's scheme to provision distant SpaceX inventory raises questions concerning a new benevolent of billionaire philanthropy: Gifting group shares in a business alternatively of cash.

Giving millions of children a interest in SpaceX could provision them prosperity to use afterward in existence — but the value is tied to a sole company's stock. And former shows that efforts to dispersed company ownership can output uneven results, from big funding returns to stakes being sold for quick cash.

Here are another historic efforts to portion prosperity or dispersed company ownership among mundane people.

US Sen. Mike Gravel's 1978 portion recommendation to Alaskans

Senator Mike Gravel (D) represented Alaska from 1969 to 1981. He ran for chairman in 2008 and 2020. Original Caption

In 1978, US Sen. Mike Gravel projected a statewide ownership company that would have stated Alaskans, including children, stakes in important energy and another endeavor projects. The Trans-Alaska Pipeline System was one possible investment.

The idea was that Alaskans would advantage from an financial surge and any rising asset values. However, the scheme never materialized.

Alaska alternatively developed a dividend scheme that draws on its preexisting Permanent Fund, a province prosperity established in 1976 alongside oil royalties that invests in stocks, bonds, and another assets. It began paying annual prosperity dividends to eligible Alaskans in 1982.

The Permanent Fund still continues today, alongside a $1,200 dividend distributed to all eligible Alaskan grownup and kid who applied in 2026. The first dividend this twelvemonth was set at $1,000, but recipients additionally got an additional $200 energy-relief payment.

Eastern Europe's 1990s bulk privatization experiment

In the first 1990s, governments throughout Central and Eastern Europe used voucher programs to rotate citizens into shareholders of erstwhile state-owned companies. MICHAEL EVSTAFIEV/AFP via Getty Images

Following the downfall of the Soviet Union in 1991, multiple countries in Central and Eastern Europe used voucher programs to transfer erstwhile state-owned companies into the hands of mundane people.

Russia began distributing privatization vouchers to approximately 144 myriad group in 1992. Russians could use them to buy shares in erstwhile province companies, toggle them for shares in funding funds, or market them for cash.

Czechoslovakia used a akin system, allowing adults to buy low-cost voucher books and use the points to bid on business shares or allocate through funds. After Czechoslovakia divided in first 1993, the Czech Republic continued its voucher program, during Slovakia canceled its planned second motion of voucher privatization.

Other countries, including Poland, Romania, Bulgaria, and the Baltic states, adopted their own versions of bulk privatization.

The programs were designed to quickly create broad community ownership. But many recipients sold their vouchers for prosperity amid financial uncertainty, fairly than retaining shares.

Evžen Kočenda, a prof at Charles University's Institute of Economic Studies, said that the program was a fine idea at the time, stated the region's mediocre economy, but that ultimately, the vouchers were not 'real money,' but fairly a financial asset.

Kuwait's 2019 national inventory toggle privatization

Kuwait privatized several of its national inventory exchange, Boursa Kuwait, to its citizens throughout 2019. YASSER AL-ZAYYAT/AFP via Getty Images

Kuwait privatized Boursa Kuwait, the controller of its national inventory exchange, in 2019, and gave Kuwaitis the chance to acquisition a interest in the company. The only requirement for registration was that the idiosyncratic was a Kuwaiti citizen.

Those who participated — concerning 83,000 of the country's 4.4 myriad — were initially allocated up to 70 shares each. The first community offering portion cost was set at 100 Kuwait fils, approximately $0.33 per share.

Following the privatization, Kuwaiti citizens and private-sector investors together owned approximately 94% of Boursa Kuwait. The Public Institution for Social Security held the remaining 6%.

The Kuwaitis who participated saw a huge return on investment: When Boursa Kuwait debuted on the community market in September 2020, its shares rosy additional than 10-fold from their 100-fils IPO price, decision at 1,055 fils, or concerning $3.41.

Today, the privatization is extensively regarded as a important achievement in the funding world, and Boursa Kuwait has described it as the Middle East's lone privately owned inventory exchange.

Faster than earcut: triangulating 3.7M map polygons in 489 ms

support@mycoding.id (Putri Silvia) — Thu, 22 Oct 2026 10:31:18 +0700

We have published the polygon triangulator of our map motor as a small library, earstrip, under the ISC license, the identical one earcut uses. It is one C++ header, and a WebAssembly build on npm that takes the identical arguments as earcut. In C++ it is 14% faster than earcut.hpp, in the browser 1.4 to 2.1 times as accelerated as earcut.js, and the comparison is near the end. This article is concerning how we got there.

A GPU fills triangles and nothing else. A map, on the another hand, is mostly polygons: buildings, parks, parking lots, the band of turf between two tram tracks. Here are a few blocks of Śródmieście in Warsaw the way our renderer says them from a map file, :

Before any of this gets a fill, all outline have to be cut into . Hover complete a polygon, or tap it, to see what it turns into. These blocks clasp 331 polygons alongside 5,151 vertices, and they rotate into 4,514 triangles.

Zoom flat 13 of our offline map of the Masovian Voivodeship, the area about Warsaw, has 3,740,425 polygons. On one center of an M4 Pro, the constrained Delaunay triangulator in our motor needs 2.93 seconds to cut all of them. This October I replaced it alongside ear clipping. To compose this article I rebuilt the ear clipper one idea at a time, one commit per idea, and timed all commit on those identical 3.7 myriad polygons, so all division below ends with a number.

Delaunay

A triangulation of a set of points is Delaunay whenever no item lies inner the circle through the three corners of any triangle. Two triangles that portion an border are adequate to see how the regulation works. Drag the item D:

Smallest viewpoint 45°; alongside BD alternatively it would be 37°

While D stays exterior the dashed circle through A, B and C, the shared border is AC. and the border flips to BD; and it flips back. Each flip is a local repair, and a triangulation anywhere no border wants to flip is Delaunay. Of all the ways to triangulate the identical points, it has the largest smallest angle. The orange arc marks the smallest viewpoint of the two triangles, and the row under the fig shows what the another diagonal would give: anywhere you put D, the edge the circle regulation picks wins.

"Constrained" method that the polygon's own edges must remain edges of the triangulation, equal anywhere the circle rule would flip them away. Our triangulator inserts the vertices one at a time. For all new item it walks throughout the triangles built so far until it finds the one the item falls into, splits it, and flips edges about the new point until the circle regulation holds again. It additionally forces the polygon's edges in, and at the end it keeps lone the triangles that lie inner the polygon and exterior its holes.

Well-shaped triangles matter whenever you interpolate item throughout them or run a simulation on them. A flat fill doesn't care: a GPU fills a needle-thin triangle as correctly as a fat one. And most of the activity is spent on polygons that barely need any. Almost fractional of the Masovian polygons, 1.8 million, have four vertices or fewer. A triangle needs no cutting at all and a convex quad is two triangles, yet the Delaunay code spends 419 ms on them, construction all triangulation from scratch.

Ears

An ear of a polygon is a border you can cut off alongside one direct cut. Take three successive vertices a, b and c. If the border at b is convex and no another part of the polygon reaches into the triangle abc, that triangle is an ear. Cut it off, and what remains is a polygon alongside one vertex fewer. In 1975 Gary Meisters proved that every simple polygon alongside additional than three vertices has at smallest two ears that don't overlap. So there is continually another ear to cut, and a polygon alongside n vertices falls distinct into n − 2 triangles.

Here is one of the buildings from the blocks above. Move the slider to cut its ears one at a time, or :

0 / 25

The green triangle is the next ear to go. The algorithm needs one test, "is this border an ear?", and the simplest type of it checks the border and afterward all another vertex of the boundary. cross is the 2×2 determinant established in all geometry codebase; it is affirmative whenever c lies to the remaining of the row from a to b, so for a border that runs counter-clockwise a affirmative cross(a, b, c) method a convex corner:

static double cross(double ax, double ay, double bx,
 double by, double cx, double cy)
{
 return (bx - ax) * (cy - ay) - (by - ay) * (cx - ax);
}
bool isEar(uint32_t v) const
{
 const Node &b = _nodes[v];
 const Node &a = _nodes[b.prev], &c = _nodes[b.next];
 if (cross(a, b, c) <= 0)
 return false; 
 for (uint32_t w = c.next; w != b.prev;
 w = _nodes[w].next) {
 const Node &p = _nodes[w];
 if (cross(a.x, a.y, b.x, b.y, p.x, p.y) < 0 ||
 cross(b.x, b.y, c.x, c.y, p.x, p.y) < 0 ||
 cross(c.x, c.y, a.x, a.y, p.x, p.y) < 0)
 continue; 
 if (!samePos(p, a) && !samePos(p, b) &&
 !samePos(p, c))
 return false;
 }
 return true;
}

A item on an border of the triangle counts as inside: if the ear touched the remainder of the boundary, cutting it off would depart a polygon that touches itself. The one elimination is a item at exactly the stance of a, b or c, which happens anywhere an outline meets itself at a sole vertex. Holes create that common.

Holes

Ear clipping plant on one closed boundary, and 11,345 of the Masovian polygons have holes, specified as courtyards and clearings in forests. The way to agreement alongside them is to cut all gap open and stitch it to the external circle alongside a bridge, an border from a vertex of the gap to a vertex of the boundary, walked formerly in all direction. The boundary afterward runs about the outside, crosses the bridge, goes about the gap the another way and comes back. There is no area between the two copies of the bridge, so the polygon is the same, but now it is one ring.

The extend must not cross anything. David Eberly's write-up of ear clipping has a neat way to find one. Take the rightmost vertex of the hole, m, and mold a ray to the right. The archetypal border the ray hits, at I, has an end P additional to the right. If no border vertex lies inner the triangle m, I, P, the extend m–P is safe. You can drag the gap around:

The tip of the dent lies in the triangle m, I, P. A extend to P would cross the boundary, so it goes to the dent.

the triangle is bare and the extend goes to P. the tip of the dent sits inside the triangle, and the extend goes to the dent instead: of the dents in the triangle, Eberly takes the one closest in viewpoint to the ray. Holes are bridged rightmost first, so a gap additional remaining can extend to one that is already part of the boundary.

Map data adds two cases of its own. Our coordinates are integers on a tile grid, so a ray alongside a grid row runs all the time; the crossing test passes over specified a vertex, and my archetypal type got exactly this case wrong. And mappers frequently diagram a courtyard that at one point. Such a gap needs no bridge: the two rings are joined at the shared vertex.

The naive clipper (−1,812 ms)

With holes bridged, the archetypal complete type walks about the boundary, tests corners alongside the isEar above, cuts all ear it finds and writes all triangle as three indices. On the Masovian polygons it takes 1.12 seconds, 2.6 times faster than Delaunay. The diagram splits all period by polygon size; hover complete a part of a bar to see its number, or click a row:

Delaunay2,931 ms

1. Naive ears1,119 ms

2. Only dents882 ms

3. Convex shortcut880 ms

4. Zig-zag strips762 ms

5. Buckets697 ms

6. No branches519 ms

7. Marked hits489 ms

up to 4 vertices5–8 vertices9–32 vertices33–256 vertices257 and additional vertices

is shorter than in all size category but the last. The 1,123 largest polygons, with 257 vertices or more, obtain 199 ms to clip and 90 ms alongside Delaunay. Each ear test there walks hundreds of vertices, and a polygon needs hundreds of tests, so the disbursal grows alongside the quadrate of its size. The remainder of this post is concerning making the ear test cheaper and needed small often.

Test lone the dents (−237 ms)

Look at which vertices can really halt a convex border from being an ear. Say several vertices lie inner its triangle, and obtain the one closest to the border itself. Its two edges can't cross the sides of the triangle, so both run back, distant from the corner, and the polygon wraps about that vertex from the corner's side. That makes it a reflex vertex, a dent. So if item is inner the triangle, a dent is inner it too, and the test lone has to appearance at the dents.

The motor keeps a catalog of them. A dent leaves the catalog whenever cutting its neighbour makes it convex, and a convex corner never turns into a dent, so the catalog lone shrinks. The test becomes:

bool isEar(uint32_t v) const
{
 const Node &b = _nodes[v];
 if (b.reflex)
 return false;
 const Node &a = _nodes[b.prev], &c = _nodes[b.next];
 for (uint32_t i = 0; i < _live; ++i) {
 const Item &p = _items[i]; 
 if (p.node == b.prev || p.node == b.next)
 continue;
 if (cross(a.x, a.y, b.x, b.y, p.x, p.y) < 0 ||
 cross(b.x, b.y, c.x, c.y, p.x, p.y) < 0 ||
 cross(c.x, c.y, a.x, a.y, p.x, p.y) < 0)
 continue;
 if ((p.x != a.x || p.y != a.y) &&
 (p.x != b.x || p.y != b.y) &&
 (p.x != c.x || p.y != c.y))
 return false;
 }
 return true;
}

The 28.1 myriad ear tests now appearance at 185 myriad points alternatively of 353 million, and the total drops to . That is small than you power expect, since construction outlines have a lot of dents. The construction alongside the arch complete has 15 of them among its 27 vertices. And the smallest polygons get slower, from 34 to 54 ms: for a triangle or a quad, sorting the corners into dents and the remainder expenses additional than the stroll it saves.

Skip tests on convex polygons (−2 ms)

A polygon without dents is convex, and all border of a convex polygon is an ear, so it can be cut without a single test. Checking for that needs care. "Every border turns left" is not enough:

Corners turning left: 5 of 5. Changes of x direction: 4. Not convex.

The turns remaining at all five corners, since it goes about its centre twice. The motor adds one additional condition. Walking about a convex ring, the edges go correct alongside the bottom and remaining alongside the top, so their x direction changes exactly twice, at the leftmost and the rightmost point. The passes; the celebrity changes direction four times, at the circled corners, and goes to the average ear tests. Both counts arrive out of the same loop that marks the dents, so the inspect expenses nearly nothing. Triangles and quads get their own shortcut before that loop.

The shortcut removes 4.4 myriad of the 28.1 myriad ear tests, but those were the cheapest ones: a circle without dents has nothing to scan. On its own it barely moves the total, , though the polygons alongside four vertices or small win rear part of what the dents disbursal them, from 54 to 48 ms. It matters additional in the next step, anywhere a convex circle becomes a sole strip.

Write strips (−118 ms)

So far all triangle expenses three indices. A GPU can additionally diagram a triangle strip, anywhere all new indicator adds a triangle made of it and the two before. A convex circle makes one band if you zig-zag throughout it:

0 1 7 2 6 3 5 4

The ear clipper can compose strips too. Cutting an ear makes the corners of its two neighbours sharper, so they are fine candidates for the next ear, and cutting one of them extends the band by a sole index. The engine tries them archetypal and keeps alternating between the two ends during the ears last. In its code the neighbours of the cut vertex are a and b:

while (true) {
 if (sideA) {
 const uint32_t z = n[a].prev;
 _strips.push_back(n[z].index);
 unlink(a);
 a = z;
 } else {
 const uint32_t z = n[b].next;
 _strips.push_back(n[z].index);
 unlink(b);
 b = z;
 }
 count--;
 sideA = !sideA;
 if (count < 3 || !isEar(sideA ? a : b))
 break;
}

When the next border is not an ear, the band ends and the stroll goes on from anywhere it stopped. The 21.5 million triangles now obtain 35.8 myriad indices alternatively of 64.5 million. The ear tests don't get fewer: the naive walk already went on at the next border following a cut, and now all band additionally ends on a test that fails, 25.0 million instead of 23.7 million. Still, the total falls to , and the largest polygons autumn from 146 to 103 ms.

Sort the dents into buckets (−65 ms)

A timber or a lake can have thousands of vertices and hundreds of dents, and all ear test motionless says all one of them. When a polygon has additional than 32 dents, the motor sorts them into buckets alongside the longer flank of their bounding box, concerning two dents per bucket. An ear test looks lone at the buckets that its triangle spans. As dents turn convex and leave, the indicator is rebuilt formerly fractional of it is gone.

Points scanned autumn from 181 myriad to 101 million, nearly all of it on the ample polygons: the polygons with 257 vertices or additional go from 103 to 57 ms. Total: .

Count without branches (−178 ms)

The iteration in the dent test exits at the archetypal dent inner the triangle. Most of the period there is none: the test reads all the dents and says yes. An first exit afterward buys nothing, and the three comparisons per item are branches the CPU has to predict. The motor archetypal runs a continue that lone counts dents inner the triangle, alongside no branches at all:

const double abx = b.x - a.x, aby = b.y - a.y;
const double bcx = c.x - b.x, bcy = c.y - b.y;
const double cax = a.x - c.x, cay = a.y - c.y;
uint32_t hits = 0;
for (uint32_t i = begin; i < end; ++i) {
 const Item &p = items[i];
 const double flank = std::min(
 std::min(abx * (p.y - a.y) - aby * (p.x - a.x),
 bcx * (p.y - b.y) - bcy * (p.x - b.x)),
 cax * (p.y - c.y) - cay * (p.x - c.x));
 hits += (uint32_t)(side >= 0) & (uint32_t)(p.node != ia)
 & (uint32_t)(p.node != ic);
}
if (hits == 0)
 return true;

The smallest of the three determinants is non-negative exactly whenever the item is inner the closed triangle. Only when the figure is not zero does the exact iteration from before run, to regulation out dents that sit on a corner. The bucket type does the identical alongside a bounding-box test, 64 points at a period into a bit mask. The total falls to , and the center sizes acquire the most: polygons alongside 33 to 256 vertices go from 327 to 216 ms.

Test lone the marked dents (−30 ms)

When the figure is not zero, the exact iteration says all the dents again, although the archetypal continue has already seen which ones were inside. The bucket type keeps that in its bit mask. The abbreviated list, up to 32 dents, can do the same: the archetypal continue sets one bit per hit alternatively of counting, and the exact test looks lone at those dents:

 hits |= ((uint32_t)(side >= 0) & (uint32_t)(p.node != ia)
 & (uint32_t)(p.node != ic)) << (i - begin);
}
while (hits) {
 const Item &p = items[begin + lowestBit(hits)];
 hits &= hits - 1;
}

Points peruse autumn from 128 myriad to 120 million. This is the final step, and it is the code of earstrip as published: , 6.0 times small than Delaunay on the identical polygons.

When there are no ears

Meisters' theorem is concerning uncomplicated polygons. Map data is rounded to a tile grid, and rounding can interrupt a simple polygon. Here is the outline of Warszawa Śródmieście, a railway station small than a kilometre from the blocks above, rounded to grids of distinct size. The orange rings mark vertices that district on the identical item as their neighbour:

Grid stage 73 cm. 68 of 154 vertices district on their neighbour. 84 triangles.

per tile side, the grid our map records use, a grid stage is 18 cm. Even there 31 of the 154 vertices merge alongside a neighbour, since mappers frequently location points a few centimetres apart, and the outline survives. nearly half of the vertices merge, and the clipper motionless cuts it. the rounded outline touches itself in a way that leaves no ear at all, and at 10 bits it happens to activity again.

When the clipper walks all the way about the border without finding an ear, it drops the corners that have no area and walks formerly more. If that fails too, it gives up, and the motor triangulates that polygon alongside the Delaunay code, which motionless exists for this. In the Masovian data 76 polygons end up there. They arrive from a map built before the final alter on the another end: our map generator now rounds all polygon to the final grid with GEOS's break rounding and checks that ear clipping can cut it. In a caller build of the Masovian map that repaired 273 polygons, and ear clipping can cut all polygon in the file.

Delaunay has one additional job. Every gap needs its own bridge, and finding one scans the entire boundary, so a polygon with thousands of holes pays for thousands of scans. Our earth map has a polygon alongside 11,675 holes: ear clipping takes 606 ms on it, Delaunay 17 ms. The motor now sends all polygon alongside 128 holes or additional to Delaunay. In the Masovian data that is two polygons.

The archive keeps the two of these fallbacks inside. earstrip covers a polygon alongside a constrained Delaunay triangulation of its circle edges whenever the ears run out, whenever the triangles they depart shield the incorrect area, and when it has additional than 128 rings; run() in C++ and lastStatus() in JavaScript say which way a polygon went. On the 56,472 polygons of our earth map tiles, alongside up to 11,675 holes in one, earstrip takes 459 ms and earcut.hpp 2,892 ms. On the zoom 13 polygons that the ears can't cut exactly, earcut's triangles differ from an even-odd inhabit of the rings on 31% of the pixels on average, earstrip's on 0.15%.

In the browser

Before the new clipper went into the app, I compared it alongside earcut, the ear clipper rearward MapLibre and Mapbox GL, in its C++ type earcut.hpp 3.2.4. Natively the two are close, and that could have been the end of it. But we are operating on a web renderer that runs the identical C++ motor compiled to WebAssembly, so I went additional and compared the browser flank too: the two clippers compiled to WebAssembly, and earcut.js 3.2.4, the JavaScript first that web maps run today. For that I took the clipper out of the engine into earstrip. On all 5.8 myriad polygons of our test sets it writes exactly the engine's strips, and its repository has these polygons and the code rearward all figure in this section. Its native benchmark is the one behind the diagram above, and the native row below is the final stage run against earcut.hpp. Every WebAssembly build gives exactly the identical triangles as its native type on all 4.4 million polygons of zoom levels 13 and 9. Milliseconds for the 3.74 myriad polygons of zoom 13, finest of 7, one thread, V8 in Node and JavaScriptCore in Bun:

	earstrip	earcut
Native C++	489	557
WebAssembly, iteration inner the module, V8 / JSC	601 / 566	930 / 843
WebAssembly, one call from JS per polygon, V8 / JSC	688 / 688	1,007 / 950
npm package, one call per polygon, V8 / JSC	766 / 807	1,103 / 1,290
npm package, straightforward calls, all polygons in one call, V8 / JSC	635 / 619

Natively earcut.hpp is 14% slower than earstrip. Compiled to WebAssembly and run in V8, earstrip loses 23% and earcut.hpp loses 67%, nearly all of it in its chief iteration that searches for ears. It isn't the allocator: builds with mimalloc and emmalloc provision the identical time. I haven't dug further. In V8, earcut.hpp in WebAssembly ends up lone 19% faster than plain earcut.js, and 10% formerly all polygon is a distinct call from JavaScript.

The bundle rows are what a web leaf gets, and the earcut pillar there is earcut.js. The call that replaces earcut takes the coordinates as JavaScript numbers and returns a new gathering of triangle indices all time: 1.4 times as accelerated as earcut.js in V8 and 1.6 times in JavaScriptCore. About fractional of what it loses against the loop inside the component goes into copying the coordinates in and turning the strips into a list, the remainder into calling into WebAssembly formerly per polygon. The straightforward calls skip all of that: the polygons go direct into the module's recollection and arrive rear as one indicator buffer of strips for WebGL 2 or WebGPU, 1.7 and 2.1 times as accelerated as earcut.js.

Some of the ideas complete are in earcut too. It additionally relies on the fact that lone a dent can obstacle an ear, but it checks that last, for all vertex that passes the bounding box and triangle tests, alternatively of keeping a catalog of dents. Above 80 vertices it links them in z-order and scans lone the extend of that curve the triangle's bounding box covers, which does the job of the buckets. What it doesn't have are the shortcut for convex polygons, the strips and the figure without branches: it writes three indices per triangle and stops at the archetypal item inside.

earstrip is on GitHub and npm, with the polygons from this article and the code to period it against earcut on your own machine.

SpaceX-Tesla merger could create a Frankenstein's monster Wall Street can't value

support@mycoding.id (Dan Defrancesco) — Thu, 22 Oct 2026 10:28:45 +0700

Win McNamee/Getty Images

Some pairings awareness inevitable. "Friends" had Ross and Rachel. "The Office" had Jim and Pam. Elon Musk has SpaceX and Tesla.

But a agreement between Musk's two elephantine companies could end up looking additional akin Frankenstein's monster than a Hollywood affection story. And investors power battle to comprehend and value it.

The SpaceX-Tesla tie-up isn't a shoo-in, but both sides are officially considering it. That raises questions concerning what's gained from a merger.

Combining the companies turns Musk's two jobs into one. Their slightly overlapping AI plans could create important efficiencies in areas akin compute and talent. Joe Ciolli, author of the First Trade newsletter, bankrupt downward the pros and cons for SpaceX and Tesla investors.

These companies have A LOT under their hoods, though. Here are fair some of the businesses or long-term bets that SpaceX and Tesla are pursuing:

Space-mission services (Falcon 9, Dragon, Starship)
Internet assistance (Starlink broadband)
Mobile transporter (Starlink Mobile)
Electric vehicles (Model 3, Model Y, Cybertruck, Roadster)
Government moon systems (Starshield)
Autonomous vehicles (Robotaxi)
Humanoid robots (Optimus)
Energy retention (Powerwall, Megapack)
Frontier AI models (Grok)
Social media (X)
Payments app (X Money)
AI infrastructure (Colossus)
AI coding tools (Cursor)

And don't ignore Musk's grandest ambition: colonizing Mars.

A conglomerate alongside specified a varied set of businesses can rotate into a headache to value.

Let's commencement simple: How would you depict a SpaceX-Tesla entity?

A area company? An AI company? A robotics company? Saying it's "a small bit of everything" power not sit fine alongside investors.

When a business plays in a bunch of industries, the market doesn't continually provision it the advantage of the doubt. There's a term for this phenomenon: conglomerate discount.

It method a company's inventory is appreciated lesser than the sum of its idiosyncratic parts. Some investors fear the business volition be too complex to manage, or too costly to run. Others concern its blended financials volition create it difficult to comprehend its true health, or stronger parts of the endeavor volition subsidize weaker ones.

A extensively cited 1995 paper on the topic established the discount to be between 13% and 15% among a example of diversified companies from 1986 to 1991.

Some academics and economists have asserted the conglomerate discount is a relic of the past. A 2024 McKinsey study stated the discount wasn't necessarily automatic. Instead, heavily diversified companies frequently had weak achievement or lacked financial transparency, which made it harder to measure their true value.

Either way, these types of companies necessitate investors alongside a broad appetite. (Or a durable stomach.)

Imagine you're enthusiastic concerning Starlink's potential, but not as eager on humanoid robots. Expensive robotics projects could suck up the prosperity you were expecting would go to Starlink. Or the prosperity generated by Starlink could subsidize the losses accumulated in an underperforming division.

It's why giant, sprawling companies are frequently small appetizing than a pure-play stock.

Ultimately, a agreement is frequently concerning marketing a imagination to investors. And there's arguably no one improved at doing that than Elon Musk.

Now he fair needs to demonstrate SpaceX and Tesla were continually meant to be together.

Trump Accounts for adults: 4 things advisors want Congress to consider

support@mycoding.id (Alex Nicoll) — Thu, 22 Oct 2026 10:23:01 +0700

Roberto Schmidt/Getty Images

Trump Accounts, created for children, may be coming to adults, too.

Brad Gerstner, an decorator of Trump Accounts, stated on Wednesday that the tax-advantaged accounts have to be rolled out to every US inhabitant under 40, although he flagged that Congress would need to continue new law to create that happen.

President Trump likewise stated on Tuesday that the White House is exploring creating an "alternative" Trump Account for adults.

"We are looking at item distinct since it really is slightly of a distinct item having to do alongside adults," Trump said.

So what power that appearance like? We said to five experts concerning how Trump Accounts could activity for adults, who they have to be for, and how they could fig into financial planning.

Ray Boshara, a elder guideline adviser at the Aspen Institute and Washington University in St. Louis' Center for Social Development, stated the accounts could be a boon to the 46% of Americans without prosperity in a withdrawal account, according to Federal Reserve data.

"I really akin the idea of all Americans having a lifelong funding document through which they can build prosperity and accomplish financial security," stated Boshara, who advocated enrolling all American children in Trump Accounts, a alter that was rolled out before this month.

He sees expanding Trump accounts as an expansion of the identical push, as lengthy as the accounts are automatically created for those adults, too.

Focus on those without withdrawal savings

There's a "pretty sturdy withdrawal funds system" in location for employees, stated Boshara, but nearly fractional of Americans don't have withdrawal savings, many since their director doesn't recommendation a plan.

Trump Accounts could "fill that huge gap," Boshara said, but dissimilar the plans for children, it may be worthwhile to concentration on those without 401(k) or akin plans.

"One of the benefits of the Trump accounts for kids is that it's universal, but I'm not so certain you'd desire it to be worldwide for an adult," Boshara said. Traditional IRAs already bounds tax deductions for several higher earners, and that's a "good thing," he added.

"That way, you're not fair giving wealthier Americans yet another tool," Boshara said.

If the goal is to assistance Americans without retirement, afterward Boshara's shove distant from universalism makes sense.

But Cem Karsan, originator of the hedge prosperity Kai Volatility Advisors, sees this as part of a broader attempt to stem the tide of populism by giving regular Americans a greater interest in the inventory market.

From that perspective, a worldwide scheme is essential.

"It's basically the infrastructure that's being laid, the pipes, for delivering ownership broadly to the masses," Karsan said.

For those alongside withdrawal accounts, a Trump Account, technically another category of IRA, could authorize them to contribute additional than the current cap, stated Stephanie Link, chief funding strategist at Hightower Advisors.

"There would be an additional tier of tax-deferred prosperity accumulation," Link said.

Don't disorder alongside benefits

One of the chief concerns alongside rolling out Trump Accounts to low-income Americans is that they could be kicked off community benefits akin food stamps and Supplemental Security Income. This wasn't an matter for children, but many benefits accessible to adults can be means-tested, and contributing to an document may create you ineligible.

Right now, for traditional IRAs, the rules are a "patchwork," Boshara said. A sole individual alongside additional than $2,000 in a traditional IRA can endure admission to Supplemental Security Income, during Medicaid rules change from province to state.

"What I'd call for is a categorical disregard," stated Boshara, definition prosperity in the accounts wouldn't figure against eligibility for community benefits.

"If you anticipate low- and moderate-income group to preserve and build prosperity through an IRA, you have to defend their community benefits."

Maybe most importantly, Boshara stated these have to be a "supplement" for Social Security.

"We never see these as a substitute for Social Security," Boshara said.

Fund them

The most transformative part of the Trump accounts so far has been the $1,000 accessible to all US inhabitant delivered between 2025 and 2028. But Boshara's biggest critique of the program is that families have to open up to obtain authorities payments and director contributions.

"Opting in is suppressing participation, particularly among lower-income families since they don't have the awareness, or they don't engage alongside the tax system," he said.

He's expecting for Treasury rules that volition activate accounts, so contributions can be added automatically, and that the $1,000 backing for newborns be continued indefinitely. And similarly, he's expecting that anyone can put money into adults' funds, akin alongside children.

"The finest item concerning Trump Accounts for kids is that there are multiple investors in somebody's future," Boshara said. This could equal contain director contributions.

"I'm not certain if all employers would contribute, but if they did, it's another funds advantage plan, HSA or 401K category of plan, which is again, additional prosperity in their pockets," Link said.

For those who already contribute to their 401(k) and another vehicles, akin Roth IRAs, which can be a improved fit for first occupation savers, the exterior backing is the big beneficiary, stated Christina Taylor, VP of tax betterment and shipment at tax fintech April.

"For adults preservation their own money, a Roth IRA or a 401(k) alongside an director equivalent is frequently motionless the stronger tool," Taylor said. "Savers should think of a Trump Account as a collector for another people's contributions, not a replacement for withdrawal accounts they already have."

Treat adults akin adults

Current Trump Accounts arrive alongside a range of restrictions that keep investors focused on the longer term, including a five-year holding duration for idiosyncratic stocks. That limitation on marketing ends whenever they rotate 18, and Boshara stated that adults have to be capable to decide what they desire their portfolio to appearance like.

"You desire capitalist choice," he said. "You desire to empower families to create fine choices."

Trump Accounts clasp lone equity investments, so grownup versions would necessitate greater diversification.

"A 10-year-old can journey out a bad market, but person five years from withdrawal needs several bonds, several cash, and a scheme for withdrawals," stated Brian Boswell, a prosperity director at Savvy Wealth.

It's a "great idea to create certain all Americans have a lifelong investing account," but the key is to justify it meets adults' needs, Boshara said.

"Why not broaden it and keep the awesome features of Trump accounts and discard the ones that don't create awareness for adults?" Boshara said

Google CEO Sundar Pichai Views Search Absorbed Into Agentic Layer

support@mycoding.id (Roger Montti) — Thu, 22 Oct 2026 10:17:18 +0700

In a latest interview, Google CEO Sundar Pichai described Google’s plans to create a deeper integration of AI agents into Search, saying that he envisions the near forthcoming as an agentic tier that unifies those experiences fairly than really merging all product.

Google’s Vision Of The Near Future

The interviewer, Alex Heath, invoked Google Spark, a merchandise introduced at Google I/O. Google Spark is a cloud-based individual delegate that is tightly unified alongside Search, using data agents in Search to behavior web research. Spark additionally integrates Search and Chrome together.

Heath asked:

“I’m inquisitive what you create of this individual delegate competition that the industry is now in alongside Muse, which is a awesome product… I recall at the final IO, you guys showed Gemini Spark and I’m curious, do you think Google needs to do additional here?

On consumer, I cognize you fair had the Gemini workspace anywhere you’re giving an delegate an e-mail, a workspace identity, all that. But in the person race, whenever you appearance at it, it’s early, but I ideate you desire Google to have equal additional of a interest in that.”

Pichai responded by explaining that Google is evolving a shared delegate infrastructure for endeavor and person applications. He afterward pivoted to the convergence of conversation and the implementation of tasks on behalf of users, followed by connecting all of that to Search, saying that Google’s imagination is one box.

Pichai’s response:

“It’s a huge area of focus. In fact, the goal we set out for Gemini, how we call it, we desire it to be a personal, proactive, and mighty assistant. It’s a center part of what we are trying to do alongside Gemini.

And you are correct in IO, alongside Spark, I think that’s the direction we are doing. We evidently said additional concerning it in our endeavor event in the environment of the Gemini agent.

To be extremely apparent between endeavor and consumer, it’s the identical model, identical harness, identical runtime. It is fair the environment of what you have in a endeavor or your individual environment is different.

And everything we announced in the event, since we’re construction it for enterprise, has world-class safety governance. Every project there, we are provisioning basically a micro VM to go complete the task, operating securely 24-7 alongside real-time monitoring, et cetera, providing enterprises alongside the governance and authority and auditability of those agents.

We’re bringing all that to consumers as well. So already you saw you have conversation and project now in Gemini.

Our imagination in the near forthcoming is it’s one box.”

Convergence Of Gemini And Search “In A Deep Way”

Google’s Sundar Pichai discussed the convergence of Gemini and Search. He explained that users are increasingly entering task-focused queries that necessitate agents to execute tasks fairly than fair provision answers. This, he remarked, is a user-driven trend that Google can see in its use data.

Pichai says that Google aims to create these agentic capabilities accessible in Search “in a beautiful profound way…” That declaration powerfully suggests that the forthcoming of Search involves profound integration alongside a individual aide in the form of Gemini.

Pichai explained Google’s imagination at concerning the 37 infinitesimal mark:

“Empirically, whenever we appearance at what group are doing in Gemini, if you were to go rear and categorize it, we see the trend already. So the percent of, if I were to call queries, which are additional agentic, has been steadily expanding complete time.

And we’re going to create all of that additional extensively accessible as we go through the next few months. And we chiefly launched all of this to Ultra and Pro users. And so we’re going to create it additional extensively available. The newer Argon models provision a huge stage change.

But you volition be capable to, again, as a consumer, conversation to Gemini, not fair in the app, but in the environment of perchance from… or you can call.

So we are focused on it from search, and we are focused on it in a beautiful profound way.”

Agentic Layer Is The Unifier

At concerning the 42-minute mark, the interviewer specifically asks if Search, AI Overviews, and Gemini volition always completely merge. Pichai answers that he doesn’t really see it as combining distinct products but additional as the agentic tier doing it all.

Right now, we have all of these Google products that are already overlapping. Pichai sees the forthcoming not in conditions of unifying distinct products but as an agentic tier that does it all for consumers.

Heath asks:

“Because correct now you’ve got these two large-scaled AI products, Gemini milliard users, hunt billions of users, but different, but overlapping in several cases AI experiences.

Do you always see them completely combining?”

Sundar Pichai responded:

“I anticipate our agents to provision that unifying cognition throughout these products. In fact, that agentic layer, fairly than try to converge or think concerning distinct products and how they arrive together, I think the agentic tier naturally ends up doing that, right?

Because your agents, they’re operating in the cloud, they’re operating 24/7, they have your context. And so they can choice up things anywhere you remaining off.

So you can commencement a project in several place. You go to the another place. Your delegate has that environment for you.

So I think the agentic tier offers us a chance to bring a unified merchandise cognition improved than always before.”

That appears akin a radically distinct earth than the one SEOs, consumers, and location owners live in today. The inquiry that comes to my intellect is: Does the agentic tier have a location for referral traffic comprised of humans, or volition the balance change toward AI agents and websites rotate into a quaint item of the past?

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Walmart’s chief people officer shares 3 green flags in a job interview

support@mycoding.id (Ana Altchek) — Thu, 22 Oct 2026 10:14:01 +0700

Donna Morris oversees complete 2 myriad employees, so she knows a powerful applicant whenever she sees one.

Walmart's chief group authoritative identified three green flags that create her lean in during a job discussion — despite of the function they're applying for. First, she told Business Insider that she looks for a genuine alignment alongside the company's intent and values.

Second, she wants to see an capability to obtain on multiple responsibilities and create things work.

"They display a track document of grit," Morris said, defining those individuals as group who have conquer adversity.

In an ideal world, she stated that method they've worked on the frontline, since so many of Walmart workforce have customer-facing roles. She stated the business looks for candidates alongside a concentration on service.

Morris, who started operating at 13 at a dry cleaner's and afterward worked at a drugstore for eight years, stated that if a applicant has never informed what it's akin to be a frontline hourly worker, it may be challenging for them to lead. Morris stated those jobs gave her purpose, and she established it difficult to resign.

Now, whenever she interviews candidates, Morris said her go-to question is "If I was to ask group who've worked for you before, what volition they inform me?" She stated the open-ended inquiry generates a genuine reply and helps her gauge whether the individual is focused on servant leadership, which is a "big green flag."

Asking this inquiry allows her to comprehend additional concerning a candidate's archetypal job or a activity experience. She stated that during graduating from an Ivy League academy is valuable, she's additional focused on hearing concerning what they've learned from activity experience.

"What was the grit they had to persevere through?" Morris asked.

The chief group officer, who worked at Adobe for additional than 17 years before joining Walmart, stated that her third green emblem is candidates who are passionate concerning making a difference.

"That, to us, would be really, really key," Morris said.

CVE-2026-111943: Compromised Intermediate Certificate Authority

support@mycoding.id (Putri Silvia) — Thu, 22 Oct 2026 09:51:31 +0700

Comments

Achieving AI-style reasoning without an LLM

support@mycoding.id (Putri Silvia) — Thu, 22 Oct 2026 09:46:58 +0700

Comments

I put Muse, Dot, and Instinct to the test. There was a clear winner, but not because it worked best.

support@mycoding.id (Katie Notopoulos) — Thu, 22 Oct 2026 09:40:01 +0700

I put Muse (left), Instinct (middle), and Dot (right) to the test. Meta / Instinct / Getty

I've been playing around alongside assorted AI agents — Muse, Instinct, and OpenAI's Dot, and I've established them genuinely really helpful for certain things.

But which one is best?

I put them to some, uh, semi-rigorous testing. Ok, not totally rigorous or controlled, but I gave them all the identical 3 tasks — triage my email, command me lunch, and build me a individual website — and it was engaging to see which worked best. It was additionally engaging to see how alternatively they functioned at these tasks.

If you haven't started using an AI delegate yet but are curious, well, I'm not certain that this is going to answer all your questions, but perchance is a location to get started.

A big spoiler current for my supreme recommendation: correct now, Meta's Muse is the lone liberated among the bunch, so in my mind, that wins for most normal group who fair desire to try an delegate out. OpenAI's Dot is lone for the $100/month scheme (ouch!) and Instinct is currently invite-only.

To me, that's a no brainer. Do the liberated one (unless you're already paying for the ChatGPT plan, in which case you're likely already a beautiful pro person and likely don't need my advice).

Challenge #1: Manage my email

Sometimes managing emails can awareness akin the communication environment in Harry Potter. Could the AI agents help? Jun Sato/WireImage

I get a lot of email. Most of it I don't need to annoy opening. Help me fig out which are the crucial ones.

Muse: Muse has been a dutiful serviceman here, and this was beautiful much the archetypal project I gave it whenever I started using it a few weeks ago. So far, "triage my email" has been the one really helpful item an delegate has done for me.

Instinct: Overall, I don't akin the fact that Instinct plant chiefly through your content messages vs. a dedicated app. While that's extremely convenient in certain ways, I don't akin the idea of seeing email (bad, no fun) in my texts (good, fun) — mixing the two feels wrong.

However, I have to hand it to Instinct — it established a Paperless Post invitation for a gathering that I would alternatively have entirely missed in my individual email. It was the lone delegate to drag that one out for me.

Dot: Dot did item really helpful that required a lot of context, and it did all on its own. It noticed an older email toggle from final duration alongside my tax accountant (I had submitted for an extension, due in October). Dot noticed my accountant never confirmed he really submitted my taxes — yikes! This really really saved me! (Don't worry, the taxes had certainly been filed.)

Finding an older — but crucial — email toggle that I hadn't closed the iteration on is exactly what I desire from an AI agent/assistant. I awareness akin 90% of things I'd fairly do myself, but my individual brain gets effortlessly distracted and forgetful, and there are plentifulness of things I ignore to agreement alongside or get distracted.

Winner: Dot. Also me, for not going to jail for tax evasion.

Challenge #2: Order me lunch.

I asked all delegate to command me a dish at a particular local restaurant. Because I didn't desire to annoy genuine humans, I did the orders for pickup, to evade any disturbance or multiple distinct deliveries.

This turned out to be a genuine fiasco, and I learned my lesson: it would have been infinitely easier for me to fair do it myself.

Muse: Initially, Muse was extremely helpful. I accidentally gave it the incorrect eatery name, but Muse helped me discover the correct location and pulled up the accurate list options for me to see.

I had already connected my credit cards alongside Stripe, so I figured this would be easy. It offered up my debit cardstock or another Mastercard, which I assumed was my regular credit cardstock (I had fair gotten a new cardstock in the communication and didn't acknowledge the final four digits).

When I eventually went to inspect my wallet, I discovered it was a error — it was really the cardstock connected to my health insurance-related HSA account, which is lone approved for medicinal expenses.

Oops, Muse was using my HSA card, which can lone be used for medicinal expenses. Business Insider

By the period I straightened this disorder out, I had already moved on to placing orders alongside Instinct and Dot. When I went to choice up my orders at the restaurant, there were lone two bags alongside my name on them. Sure enough, I had gotten so distracted alongside the another orders that I never confirmed alongside Muse concerning placing the order, and it never sent through. (Can you see now what I was saying before concerning how I get distracted and don't complete tasks?)

Instinct: The most annoying part concerning Instinct, again, in my opinion is that it's complete content messages. That method there were several quirks whenever I tried to content on my laptop — sometimes it unsuccessful to dispatch as SMS. But following a few fails, Instinct was capable to location the command comparatively quickly. Well, kind of.

All told, this took 9 distinct content messages on my end — not including Instinct's responses. It is difficult for me to ideate that this was at all easier than fair going onto the restaurant's website or UberEats and doing it myself.

Although the procedure was fairly painless, there was one item I didn't like: it put the another through alongside no tip (!!!) and didn't ask me if I wanted to add one.

Dot: Somehow, this was the most difficult to get to work. It kept getting tripped up by the 6-digit code sext by content needed to login, but it kept failing (I had to get the code texted 3 distinct times), and the browser it created for me to act wouldn't let me category into it. A mess, basically, but eventually it worked.

Winner: None. I awareness extremely assured in saying that it would have been infinitely easier to fair location the command myself through regular means. It's imaginable that it would be easier to command in the future, since I've already done the activity of environment it up once. But I've learned my lesson.

Challenge #3: Build me a website

I have an old personal/professional website, but it's due for a refresh. I have awful imaginative awareness and don't desire to expend much period designing a site. Seems akin a fine job for AI to do for me!

The individual website Muse made for me feels horrifically cringe. Muse / Business Insider

Muse: I appreciated that, dissimilar Dot and Instinct, Muse didn't use the photo of my visage from my byline photo (it seems extremely weird to have a elephantine photo of your own visage on a expert website).

But all three basically did the identical thing: pulled the info from my author bio on Business Insider, selected several latest articles as clips, beautiful essential stuff.

Instinct's website it made for me. Pretty essential (and weird photo!) Instinct / Business Insider

Instinct: The scheme current was the most boring, and in a way I wouldn't equal cognize how to create it appearance improved (terrible imaginative sense!). Weirdly, the selected part it pulled for me were random ones from complete a twelvemonth ago.

Dot: The appearance and scheme were likely the finest here, and it pulled a decent mix of articles I'd desire to showcase. But GOOD GRIEF the tagline current is so corny that I'm kind of embarrassed to equal display you, equal although I didn't compose it myself:

"Technology, business, and existence on the internet." CRINGE!!!! Business Insider / Dot

Winner: Based on the mockups it made alongside scheme alone, I'd go alongside Dot. I think I could activity alongside this. But all three of these made specified cringey, cheesy copy concerning me that it honestly made me awareness genuinely depressed. Is this who I am? Yuck!

Obviously, I'd have to rewrite all the copy myself, and choice the articles for clips — and at this point, is this equal preservation me any period beyond being a extremely generic template? Ah well.

Conclusion: The biggest difficulty alongside all of them was figuring out what benevolent of project an delegate is well-suited for.

Most of all, I established that trying to oversee three distinct agents at the identical period was a huge hassle. I desire AI agents to be akin a wife — making my existence easier by anticipating my needs, not a hubby who merely does chores whenever told. But this has backfired on me: now I have three wives, and they're all nagging me and I'm forgetting which is which. Ultimately, I don't propose AI polygamy. May my test alongside it assistance you choose your bride.

Barclays is cutting 5,000 jobs in an AI push

support@mycoding.id (Alice Tecotzky) — Thu, 22 Oct 2026 09:38:35 +0700

Barclays is cutting about 5% of its workforce. KHemaz/GVN via Getty Images

Barclays stated it's cutting 5,000 jobs, or about 5% of its full-time workforce, as it increases its reliance on AI.

The British financial institution stated in its third-quarter earnings declaration that it is "structurally reducing" its expenses through "technology-enabled" caput figure reductions.

Barclays laid out the numbers in its accompanying earnings presentation, linking the cuts specifically to "organisational reduction and innovation alter effectiveness driven by AI."

Around 60% of the cuts volition effect assistance functions, including developers, according to the presentation.

Wall Street general is grappling alongside how AI volition effect caput figure for the two back- and front-office roles, as much of younger bankers' technical, repetitive workload can be effortlessly automated.

Goldman Sachs stated in a memo final year that it plans to "restrain caput figure growth," and JPMorgan CEO Jamie Dimon has stated he volition apt hire small bankers and additional "AI people."

This is a evolving story. Check rear for updates.

The AI safety bottleneck: Save the world or get the bag

support@mycoding.id (Amanda Hoover) — Thu, 22 Oct 2026 09:37:01 +0700

Getty Images; Tyler Le/BI

When Rishub Jain worked on safety at Google DeepMind, he held among the most coveted jobs in tech. He was among a small collection of researchers working to mitigate the most consequential risks of AI.

But he remaining to cofound a nonprofit, Sampura Research. Jain traded the prestige and admission that arrive alongside a Google job since he feels he can have a sharper, additional profound effect on AI safety from the outside. Jain says he wants the autonomy to prosecute investigation on a particular matter in AI alignment, which he felt had been deprioritized.

"All the top AI companies do attention a lot concerning safety, and they allocate in it, but they could be investing a lot more, and that's benevolent of what was inhibiting me," Jain says. "If they invested additional in safety, I would motionless be at the lab and operating on what I'm curious in alongside a ample team." Google did not react to a petition for comment concerning whether it maintains a concentration on this particular area of research, but DeepMind's principles province that it maintains "a holistic method to responsibility, safety and security."

Jain's originator narrative echoes concerns raised by safety researchers who have discontinue OpenAI and Anthropic, including dire warnings that these companies are "gambling alongside our lives," and could create innovation that ends humanity. Once dismissed as fringe and doomsday-obsessed, the AI safety occupation is expanding as hacks by rogue agents and a cord of departures have vaulted the matter into mainstream concern.

Open roles in AI safety jumped by 91% on LinkedIn complete the former year. The industry expects a flood of prosperity from much-anticipated IPOs at Anthropic and, potentially, OpenAI, as those made absurdly affluent by the equity returns may chimney prosperity into possible clean-up crews for the messes created by the technology's fast development.

The AI safety problem is existentially big, but the group who have spent period honing AI ability are few. These are group who test and train AI systems to run ethically. Some concentration on specialized research, others on guideline and governance. And they're increasingly sounding alarms.

"The group who are most concerned concerning AI are frequently the group who have been closest to the technology," Laura González Salmerón, advising director at 80,000 Hours, a nonprofit focused on transitioning group into careers alongside elevated social impact. The scarcity matter is small concerning finances and additional concerning people. "Talent is the chief bottleneck."

Startups and nonprofits in AI safety must fight for talent in a shallow pool.

Jain's Sampura lists a pay range of $273,000 to $395,000 for an informed squad leader, which would put that employee in the top 3% of earners in the US, but apt pales in difference to offers from larger companies. Meta reportedly offered $100 myriad in signing bonuses final twelvemonth to poach the world's top experts. OpenAI gives workforce an average of $1.5 myriad in stock-based compensation, and technical personnel at Anthropic can acquire additional than $1 myriad annually.

Money aside, researchers are divided complete whether they're additional productive when operating inner tech companies to mitigate risks as the latest models develop, or strolling rear and attempting to steer the container toward safer outcomes from the outside, akin Jain did.

"I'm a small bit concerned concerning the gait of AI, and if we're capable to really arrive up alongside item helpful before the rug gets pulled below us," Jain says. He's concerned that AI's advancement volition outpace the activity to keep it in row alongside individual values. "That's benevolent of what's been happening already."

David Robinson, who led transparency activity for OpenAI's safety team, wrote an composition for The Atlantic explaining his departure. He stated that AI companies should have additional layers of checks and safety measures, akin to those in airports and nuclear energy, fairly than the steamrolling civilization of Big Tech.

"Perhaps I should have stayed and clashed for essential shifts in our staffing and culture, but in practice, my colleagues and I were so occupied sprinting that we rarely had the chance to regard big changes, much small to really create them," he wrote. Robinson has stated he plans to activity on these issues from exterior of the company.

As the matter grows in prominence, so has a niche of organizations assisting train and enlist workers — and the opportunities aren't fair for AI experts. Neav Topaz, co-director of Kairos, a nonprofit that upskills group to activity in AI safety and lately raised $50 myriad from Coefficient Giving, says he speaks alongside concerning 10 to 20 group all duration who desire to move to AI safety, and involvement is growing. He tells me that group are asking him additional frequently concerning what the activity entails.

"People are observing AI get fine at their own jobs and starting to ask themselves what comes next," he says. The emerging jobs in AI safety "pay well, the site is expanding fast, and you get to activity on what power be the defining issue of our time."

Kairos this summer launched its Generator Residency, a cohort of 27 group who spent three months training for occupation transitions into AI safety. It's small concerning the investigation and specialized activity and additional concerning merging another expertise, akin in consulting, alongside AI safety principles.

He says that of the 27 residents, a dozen have already landed roles in the AI safety space, and many of the remainder have offers to do so.

"Most of the group who volition do this activity are doing item alternatively today," Topaz says. "The biggest endowment gap in the site isn't researchers. It's group who grasp how elevated the stakes are and can translate their varied ability into things that could create a difference in this space."

The important AI companies keep that safety is a top priority. Last month, Anthropic CEO Dario Amodei wrote an composition calling for a slowdown in AI development, and Sam Altman and Elon Musk have accepted alongside him. Amodei and Altman have the two stated lately that they volition bring third-party evaluators into their companies to bolster safety protocols.

These companies have their own safety teams, but safety activity competes alongside endeavor priorities to build the most advanced models and win in the market. The tension, alongside alongside safety issues akin OpenAI's rogue agents hacking Hugging Face, has led to additional scrutiny of inner safety work. Within days of Robinson's departure, OpenAI fired three researchers, claiming they shared delicate inner data improperly alongside third-party AI safety researchers. The ousted researchers wrote a letter, reported by The Wall Street Journal, to OpenAI's board, arguing that their involvement alongside safety researchers cut inside the range of their roles, and that their terminations were "chilling those who remain at OpenAI." OpenAI stated that the decisions "were not concerning raising safety concerns or speaking out," and that the business would not blaze group for doing so.

While several have obsessed complete AI risks for years, 2026's breakthrough instant has accelerated backing for and involvement in the industry. 80,000 Hours has had calls alongside several 8,000 group since its founding in 2011, the bulk of which have focused on AI in several way, González Salmerón says. Philanthropic groups akin Coefficient Giving recommendation enormous grants to startups in the AI safety space. METR, a nonprofit AI safety collection that investigated the OpenAI agentic hack on Hugging Face, says it raised $71 myriad in six months this year.

Much of the backing flowing into AI safety filters through effective altruism, the Silicon Valley-tied philanthropic ideal focused on using evidence and logic to donate prosperity anywhere it volition accomplish the most good. Critics say the doctrine can location too much accent on avoiding forthcoming problems fairly than addressing imminent needs. With additional alarms sounding about the existential danger of AI, additional group perspective those theoretical, distant fears as urgent causes.

AI safety groups and idiosyncratic donors have spent nearly $50 myriad in this year's ballot on candidates focused on regulating AI, Axios reported. Jaan Tallinn, a billionaire who invested first in Anthropic and could shortly have equal additional billions as a result, has donated $170 myriad to nonprofits focused on AI safety. Tallinn's view, as he lately told The Wall Street Journal, is that he can have the biggest effect on the forthcoming of AI and its risks by investing in AI companies, and afterward donating returns to nonprofits operating in the AI safety space.

Coefficient Giving, the philanthropic project from Facebook cofounder Dustin Moskovitz established in the productive altruism movement, announced its AI-safety-focused arm, Project Tailwind, in September. It says it's on track to provision additional than $1 milliard this twelvemonth to assistance companies of all sizes measure up in preparedness for what it believes volition be a deluge of philanthropic capital. Sampura Research has received an $11 myriad aid from the group. If startups don't commencement hiring now, the logic goes, they won't move quickly adequate whenever enormous prosperity hits the industry.

The dizzying betterment of AI has created safety risks, and there are "gaps in the ecosystem that aren't being filled and that aren't being owned by people," says Rossa O'Keeffe-O'Donovan, the program director in specialized AI safety at Coefficient Giving. There are few people, he says, who sit at the intersection of specialized expertise, hazard analysis, a solutions-oriented vision, and the capability to hustle as a startup founder.

New roles volition appear not fair for specialized talent, but additionally for consultants, guideline wonks, communications experts, and founders who can run a endeavor alongside specialized experts. Working from the outside, they'll visage hurdles in purchasing compute power, a deficiency of straightforward admission to the latest AI models, reduced leverage, and smaller paydays.

The earth feels unmoored, and the forthcoming perchance additional malleable than ever. The petition of doing activity that does fine could stave off feelings of uncertainty, dread, and anxiety that group increasingly awareness toward AI. In one doomer camp, AI volition obliterate white-collar jobs. The optimistic perspective is that AI drastically reshapes activity and spurs the innovation of jobs we haven't yet idea of. AI safety is among the first.

"There are many of group beyond AI researchers who can help, akin cybersecurity experts, akin biosecurity experts, akin economists," says Mantas Mazeika, a investigator alongside the Center for AI Safety, a nonprofit focused on AI investigation and advocacy. "They're making extremely important contributions that the clean AI researchers, who were most of the group focusing on the issue beforehand, would be entirely unable to do on their own."

Business Insider's Discourse stories provision perspectives on the day's most pressing issues, informed by analysis, reporting, and expertise.

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Amanda Hoover is a elder equal at Business Insider covering the tech industry. Amanda covers several of the world’s biggest tech companies akin Airbnb, Spotify, and Meta and its influential figures. She has written concerning the evolving workforce, from the ways AI is reshaping roles to how workforce are changing their relationship to the job or how they appearance for work. Her reporting includes profound dives into the tech workforce, looking at the increase of product managers, chief medicinal officers, and how ageism disproportionately affects tech workers. Amanda additionally writes concerning shifting tech trends, and how group are reevaluating how they expend screen time or what apps they use to encounter new people. She has written concerning the power of social media platforms akin Twitter as fine as TikTok trends and YouTube creators.Prior to joining Insider, Amanda covered accommodation tech and the forthcoming of activity at WIRED. She has additionally written for The Star-Ledger, The Boston Globe, and Morning Brew.

Forkiva v2.2.0 – Restaurant POS & Management System Script

support@mycoding.id (Null Php Script) — Thu, 22 Oct 2026 08:40:00 +0700

Forkiva v2.2.0 – Restaurant POS & Management System PHP Script Free Download. Run Every Part of your Restaurant from one Powerful Platform. Forkiva is a Complete Point of Sale and Restaurant Management Solution built for Independent Venues, Growing Hospitality Businesses, and Multi-branch Restaurant Groups.

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A Markdown File Should Just Open

support@mycoding.id (Adi Gunawan) — Thu, 22 Oct 2026 08:21:52 +0700

The satisfied you are looking for cannot be found.

Back home

Bagaimana AI Mengubah Dunia Marketing?

support@mycoding.id (Mc Project) — Thu, 22 Oct 2026 07:00:34 +0700

Bagaimana AI Mengubah Dunia Marketing?

Channel ini menyajikan materi seputar Data Science & Artificial Intelligence, Networking & Cyber Security, serta Database & Programming. Seluruh materi disusun secara sistematis untuk membantu Anda memahami konsep, teknologi, dan praktik yang digunakan di bumi teknologi informasi, mulai dari tingkat alas hingga lanjutan. Setiap bahasan berfokus pada pengembangan keahlian melalui materi yang gampang dipahami, relevan dengan kebutuhan industri, dan disampaikan untuk tujuan edukasi. Seluruh konten mengedepankan implementasi teknologi secara legal, etis, dan bertanggung jawab, sehingga dapat menjadi rujukan belajar bagi pelajar, mahasiswa, maupun profesional. Jika Anda mau mengembangkan kapasitas di sektor pemrograman, pengelolaan data, kecerdasan buatan, jaringan komputer, maupun keamanan siber, channel ini menyediakan materi yang dapat membantu membangun fondasi ilmu sekaligus menaikkan keahlian secara bertahap. Playlist video terkait: ▪ https://www.youtube.com/playlist?list=PLZGgeqmsm32s ▪ https://www.youtube.com/playlist?list=PLZomBNpDl3vc ▪ https://www.youtube.com/playlist?list=PLE5VyXMZkzb8 PEMBAHASAN MATERI Playlist Materi Ilmu Komputer berisi kumpulan video pembelajaran, tutorial, klarifikasi konsep, studi kasus, demo, dan bahasan komplit seputar beragam sektor dalam ilmu komputer. Materi yang disajikan mencakup alas hingga tingkat lanjut, mulai dari pemrograman, pengelolaan database, jaringan komputer, keamanan siber, mekanisme operasi, pengembangan alat lunak, arsitektur komputer, hingga beragam teknologi yang digunakan dalam bumi komputasi modern. Temukan bahasan mengenai komputer dan pemrograman, algoritma, struktur data, PHP, JavaScript, Python, Laravel, MySQL, PostgreSQL, API, rekayasa alat lunak, database, jaringan komputer, pengelolaan server, cloud computing, virtualisasi, Linux, Windows Server, keamanan jaringan, cyber security, ethical hacking, kriptografi, keamanan aplikasi, DevOps, Internet of Things (IoT), kecerdasan buatan (AI), machine learning, serta beragam konsep, teknologi, dan praktik terbaik di sektor ilmu komputer. Playlist ini cocok bagi pelajar, mahasiswa, programmer, web developer, software engineer, pengelola sistem, ahli IT, maupun siapa saja yang mau mempelajari dan memperdalam wawasan di bumi ilmu komputer secara menyeluruh. CONTACT US ▪ Email support@mycoding.id ▪ WhatsApp https://wa.me/6281274309663 ▪ Telegram https://t.me/mycodingxd OFFICIAL LINKS ▪ Web https://mycoding.id ▪ Blog https://blog.mycoding.id ▪ News https://en.mycoding.id ▪ Apps https://play.google.com/store/apps/dev?id=5237501547073493079 SOCIAL MEDIA ▪ https://linkedin.com/company/mycodingxd ▪ https://facebook.com/mycodingxd ▪ https://instagram.com/mycodingxd ▪ https://tiktok.com/@mycodingxd ▪ https://twitter.com/mycoding_xd ▪ https://pinterest.com/mycoding_xd CHANNEL LINKS ▪ YouTube https://safelink.asia/youtube ▪ DailyMotion https://safelink.asia/dailymotion ▪ WhatsApp https://safelink.asia/whatsapp ▪ Telegram https://safelink.asia/telegram Powered by https://linkedin.com/company/401XDGroup #CyberSecurity #ArtificialIntelligence #Networking #DataScience #Programming #Database

The director behind Yung Lean's viral 'Storm' video says AI imitations on social media made even good work look 'lame'

support@mycoding.id (Amanda Goh) — Thu, 22 Oct 2026 06:56:32 +0700

The director of Yung Lean's viral "Storm" video says AI made his activity appearance "lame." Francis Specker/CBS via Getty Images

The director rearward Swedish rapper Yung Lean's viral "Storm" video says AI nearly drove him from filmmaking.

In an interview with Vulture published on Tuesday, Romain Gavras stated he was disheartened by the motion of AI-generated copies of the music video.

Gavras directed the video for "Gener8ion," an audiovisual project he runs alongside French manufacturer Surkin.

The video, which has amassed complete 40 myriad views since its publish in April, follows Lean as a intimidate at a boarding academy before the boys he has been tormenting interrupt into an detailed dancing sequence about him during a category photo.

In September, social media users began using AI to remake the dancing environment from "Storm," swapping Lean and the schoolboys for a broad range of new characters, including animals and celebrities.

"My female sent me the archetypal one I saw," Gavras said. "She was like, 'Look, this dog is doing it.'"

It was comic for a while, he said, but the novelty shortly wore off as the copies seemed to dispersed everywhere.

"It made me sad for all the group who put in so much activity on our video," Gavras said. "A trend akin this can create equal fine activity appearance lame. Part of me wanted to discontinue filmmaking, move rear to Greece, and rotate into an oliva farmer."

Gavras stated he wasn't trying to create any commentary on AI, but whenever group started calling "Storm" the benevolent of video AI couldn't drag off, he established himself drawn into the debate.

"The tech group were like, 'Oh yeah? AI could never?' Then they started replicating it. So we were forced into this conversation. It's a nightmare," Gavras said.

He added that he has turned downward a few AI companies that have approached him concerning licensing the video.

"There's an financial example coming that we didn't desire to be a part of," he said.

However, equal as AI-made versions of his activity proliferate online, Gavras stated the innovation can imitate elements of the video but has yet to create item alongside the identical originality or individual spark.

"They're missing the swagger of Yung Lean. He looks akin a hazardous baby. There's item in the imperfection of humanity, and the inner existence of person who dances or equal person who fair smokes a cigarette, that's not fairly there yet," he said.

Last month, Lean and the dancers performed the choreography at the MTV Video Music Awards. A video of the achievement has since racked up nearly 14 myriad views.

Gavras is not the lone filmmaker who has weighed in on the effect of AI on imaginative work. Other major Hollywood directors have raised akin concerns: Guillermo del Toro has stated AI could rob forthcoming filmmakers of the chance to study their craft, during Tim Burton has stated it strips distant the humanity rearward art.

OpenAI CLI

support@mycoding.id (Riki Setiawan) — Thu, 22 Oct 2026 06:23:11 +0700

The authoritative CLI for the OpenAI REST API.

brew instal openai/tools/openai

To test or instal the CLI locally, you need Go type 1.26 or afterward installed.

go instal 'github.com/openai/openai-cli/cmd/openai@latest'

Once you have run go install, the binary is placed in your Go bin directory:

Default location: $HOME/go/bin (or $GOPATH/bin if GOPATH is set)
Check your path: Run go env GOPATH to see the basis directory

If commands aren't established following installation, add the Go bin directory to your PATH:

# Add to your casing overview (.zshrc, .bashrc, etc.) export PATH="$PATH:$(go env GOPATH)/bin"

The archetypal eligible interactive openai run quietly saves depiction picker Tab shortcuts for forthcoming Bash, zsh, and seafood sessions. Open a new terminal to use them. See shortcut setup and removal for requirements and opt-out. PowerShell keeps normal Tab completion; media Enter after openai images create to open the picker.

Bare openai checks for newer stable releases whenever stdin, stdout, and stderr are terminals. It adds a plain-text notice below help, at most formerly all seven days. Explicit help, commands, completion, CI, and redirected streams skip the inspect entirely.

The notice includes an update command whenever the CLI can verify its facility method. Verified Homebrew installations use beer upgrade --cask openai/tools/openai. Go commands necessitate a verified binary, an accessible Go command, and an definitive matching GOBIN. Custom GOOS or GOARCH settings choose the facility guide instead. Go upgrades to important type 2 or afterward additionally choose the facility guide. Unknown installations obtain the installation-guide nexus instead. The CLI never installs updates automatically.

Checks use community GitHub publish metadata without API credentials, at most formerly daily. Checks regard HTTPS_PROXY and NO_PROXY, including their lowercase forms. Update finding waits at most 200 milliseconds. Failed checks remain silent. Package-manager preparedness can lag a new GitHub release. Set OPENAI_CLI_NO_UPDATE_CHECK=1 to disable checks and notices.

After cloning the git repository for this project, you can use the scripts/run manuscript to run the tool locally:

The CLI follows a resource-based command structure:

openai <resource> [subresource...] <command> [flags...]

Standard API endpoints necessitate an API key:

export OPENAI_API_KEY="sk-..." openai responses create \ --input "Say this is a test" \ --model gpt-5.5

Admin endpoints necessitate an admin API key:

export OPENAI_ADMIN_KEY="sk-admin-..." openai admin institution use completions \ --start-time 1735689600 \ --end-time 1735776000 \ --bucket-width 1d

Browse all collection alongside --help, for example openai admin --help or openai admin institution --help. Existing colon commands specified as openai admin:organization:usage completions continue to work.

Run openai to browse command groups by task, alongside descriptions for all group. Group assistance separates actions from nested command groups and lists all contiguous command. Help plant without an API key or network connection:

openai help setup # Enter your API key safely openai images create --help # Example and all depiction option openai help images create # The identical complete depiction help openai --help # Command groups and earth options

Add --help (or -h) to any command for its complete assistance page. openai assistance COMMAND shows the identical page. Command pages catalog earth options. Use openai --help for their complete descriptions. You can location --help following valid command options. The old assistance --all orthography prints immigration direction and the complete page. Command behavior, defaults, and output formats remain unchanged. Tab completion follows the identical collection command and supports assistance topics and document input paths. After upgrading, reload casing completion to allow the new file-path behavior. Older loaded completion scripts keep their former behavior until refreshed.

Enable command insertion to inhabit your command line from examples, setup guidance, and improvement commands. Ctrl+X, afterward Tab searches suggestions without operating them. Double Tab is an optional binding for Bash, zsh, and fish.

Successful JSON responses imprint readable content by default, including in pipes. Use --format json or --format jsonl in scripts that parse API data. See reading command results for extraction, binary output, and the range of readable presentation.

Upload a local document alongside an definitive purpose, inspect its metadata, afterward download its contents:

openai records upload "upload space.txt" --purpose user_data openai records get file-example openai records download file-example --output "downloaded copy.txt" openai records download file-example > copy.txt

Replace file-example alongside the returned document ID. Upload takes a plain path, without an added @. create, retrieve, content, and their existing flags remain available. See working alongside files for literal filenames, output formats, and manuscript examples.

openai images create --prompt "A small orange robot" saves images to ~/Downloads/gpt-images/ and prints their paths, including in pipes. The CLI uses gpt-image-2.5-sunburst whenever preservation without an definitive example or legacy response format. Use --model for another exact ID, --name for a filename, or --output-dir for an existing directory. Existing records are kept. --format json returns API data without preservation or applying the CLI preset. images edit additionally saves new results during keeping origin files. images create-variation is former and no longer available. To create a variation, use images edit alongside a GPT Image example and a prompt. See image generation and saving for commands and defaults.

openai images preview "path/to/image.png" redisplays a saved depiction without an API request. Use openai images inline on or off to recall whether future saved images appear automatically. Per-command --inline auto, on or off overrides that setting. on additionally permits an depiction font in local Apple Terminal.

Errors imprint actionable summaries to stderr by default. Use --format-error json for first API error details, including unidentified sectors and SDK-reported stream error events. For those events, extraction paths contain the error envelope, for example --transform-error error.code. Explicit --format values json, jsonl, raw, and yaml additionally use to errors unless overridden by --format-error; an definitive --format-error car or content restores summaries. --transform-error extracts error data independently of prosperous output. Malformed or bare error bodies obtain a harmless diagnostic; HTTP position is included only whenever the SDK retains it. Summaries omit rejected values and petition URLs; explicit API error particulars can merge server-supplied delicate data.

Environment variable	Required	Default value
OPENAI_API_KEY	no	null
OPENAI_ADMIN_KEY	no	null
OPENAI_ORG_ID	no	null
OPENAI_PROJECT_ID	no	null
OPENAI_WEBHOOK_SECRET	no	null
OPENAI_CUSTOM_HEADERS	no	null
OPENAI_MTLS_CLIENT_CERT_FILE	no	null
OPENAI_MTLS_CLIENT_KEY_FILE	no	null
OPENAI_UNTRUSTED_STDIN	no	false

--api-key (can additionally be set alongside OPENAI_API_KEY env var)
--admin-api-key (can additionally be set alongside OPENAI_ADMIN_KEY env var)
--organization (can additionally be set alongside OPENAI_ORG_ID env var)
--project (can additionally be set alongside OPENAI_PROJECT_ID env var)
--webhook-secret (can additionally be set alongside OPENAI_WEBHOOK_SECRET env var)
--mtls-client-cert-file (can additionally be set alongside OPENAI_MTLS_CLIENT_CERT_FILE env var)
--mtls-client-key-file (can additionally be set alongside OPENAI_MTLS_CLIENT_KEY_FILE env var)
--help - Show command row usage
--debug - Enable debug logging. This includes HTTP request/response particulars and bodies; do not portion debug logs if they may merge delicate payloads.
--version, -v - Show the CLI version
--base-url - Use a tradition API backend URL. HTTPS is required apart from for localhost or literal loopback IP addresses specified as 127.0.0.1 and ::1. This additionally applies to OPENAI_BASE_URL; distant HTTP backends must allow HTTPS.
--header, -H - Add a literal petition header as Name: Value; reiterate for multiple headers
--format - Change the output format (auto, text, explore, json, jsonl, pretty, raw, yaml); car selects readable content unless extracting data
--format-error - Change the stderr error format (auto, text, explore, json, jsonl, pretty, raw, yaml); car selects readable summaries unless extracting error data
--transform - Transform the data output using GJSON syntax
--transform-error - Transform the error output using GJSON syntax

Use --header or -H before or following a asset command:

openai --header 'X-Trace-ID: example-123' models catalog \ -H 'X-Client-Label: local,manual'

Values are literal: commas and colons are preserved, and @ prefixes do not read files. Surrounding spaces and tabs are trimmed from values. -H 'X-Example:' sends an bare value. If a name is repeated, the final value wins despite of case, overriding default and endpoint-specific headers. Headers managed by the HTTP transport, specified as Host and Content-Length, keep the transport's behavior. Supplied header values are hidden in assistance and redacted in debug logs.

For credential-bearing headers, have a concealed director or trusted launcher set OPENAI_CUSTOM_HEADERS immediately in the CLI's environment. This keeps credentials out of command arguments and avoids typing literal secrets into casing history. The changeable accepts one Name: Value admission per line. For example, with non-sensitive values:

export OPENAI_CUSTOM_HEADERS='X-Trace-ID: example-123 X-Client-Label: local,manual' openai models list

--header and endpoint-specific header flags override matching environment entries. Environment header values are additionally redacted in debug logs. Use OPENAI_API_KEY and OPENAI_ADMIN_KEY for norm API and admin authentication.

Mutual TLS is currently in beta. To opt in and activate a CA certificate for your institution or project, prosecute the OpenAI Mutual TLS Beta Program instructions.

To authenticate API-key requests alongside a mutual TLS client certificate, provide the client certificate and personal key as distinct PEM files. If certificate chain assistance is enabled for your organization, the certificate document must contain the leaf certificate first, followed by any intermediate certificates. Otherwise, use a client certificate signed immediately by an activated CA certificate.

export OPENAI_MTLS_CLIENT_CERT_FILE=/run/secrets/openai/client-chain.pem export OPENAI_MTLS_CLIENT_KEY_FILE=/run/secrets/openai/client.key export OPENAI_BASE_URL=https://mtls.api.openai.com/v1 openai records list

Both mTLS records must be configured together. Keep the personal key in a permission-restricted file; do not put private-key contents immediately in command arguments or surroundings variables. The CLI does not automatically choose an mTLS endpoint, so configure OPENAI_BASE_URL or --base-url explicitly. HTTP and SOCKS proxies remain supported, but HTTPS proxies are rejected to prevent presenting the client certificate during the proxy's own TLS handshake.

Passing records as arguments

For JSON input values, @myfile.ext says local document contents:

Binary upload flags use plain paths. For example, use records upload --file "upload space.txt" --purpose user_data. Here, --file "@upload space.txt" names an genuine document starting alongside @; it does not develop another document reference. See working alongside files.

openai <command> --arg @abe.jpg

Files can additionally be passed inner JSON or YAML blobs:

openai <command> --arg '{image: "@abe.jpg"}' # Equivalent: openai <command> <<YAML arg:  image: "@abe.jpg" YAML

When piping JSON or YAML from an untrusted source, allow untrusted-stdin mode:

untrusted-producer | OPENAI_UNTRUSTED_STDIN=1 openai <command> --file ./upload.txt

In this mode, values supplied through stdin are treated as literal data: @, @file://, and @data:// references in petition bodies, headers, and query parameters do not peruse local files. File-upload parameters must be provided explicitly as command-line flags. Explicit flags keep their normal file behavior and obtain precedence complete values supplied through stdin. Leave OPENAI_UNTRUSTED_STDIN unset whenever using trusted heredocs or another trusted piped input that intentionally references local files.

If you need to continue a cord literal that starts alongside an @ sign, you can escape the @ sign to evade accidentally passing a file.

openai <command> --username '\@abe'

For JSON endpoints, the CLI tool does filetype sniffing to decide whether the file contents have to be sent as a cord literal (for plain content files) or as a base64-encoded cord literal (for binary files). If you need to explicitly send the document as either plain content or base64-encoded data, you can use @file://myfile.txt (for cord encoding) or @data://myfile.dat (for base64-encoding). Note that complete paths volition commencement alongside @file:// or @data://, followed by a third / (for example, @file:///tmp/file.txt).

openai <command> --arg @data://file.txt

Linking distinct Go SDK versions

You can nexus the CLI against a distinct type of the OpenAI Go SDK for betterment purposes using the ./scripts/link script.

To nexus to a particular type from a repository (version can be a branch, git tag, or commit hash):

./scripts/link github.com/org/repo@version

To nexus to a local copy of the SDK:

./scripts/link ../path/to/openai-go

If you run the nexus manuscript without any arguments, it volition default to ../openai-go.

Please portion bug reports and characteristic requests through GitHub issues. Pull requests are constricted to repository collaborators; we do not obtain drag requests from non-collaborators. See CONTRIBUTING.md for the contribution guideline and betterment guide. For safety vulnerabilities, prosecute SECURITY.md.

This project is licensed under the Apache License 2.0.

Japan's aging business owners are selling their legacies for cheap to young entrepreneurs who are down to hustle

support@mycoding.id (Amanda Goh) — Thu, 22 Oct 2026 06:13:09 +0700

Aging owners are putting Japan's small businesses up for sale. These young entrepreneurs are strolling up. Provided by Takayuki Otawara.

Six days a week, Kengo Ohama is up before the sun. He takes 5 minutes to get ready, and is shortly out the entrance for an hourlong motorbike journey to the neighboring town of Mihara in Hiroshima, Japan.

By 6 a.m., he pulls up exterior a log-cabin-style building, residence to Country Road, the café and roastery that he's been managing alongside his spouse since April.

Kengo Ohama took complete Countryroad, a café in Hiroshima, Japan, first this year. Provided by Kengo Ohama.

He cleans the space, makes pastries, roasts coffee beans, and serves guests throughout the day. After the final array has cleared, Ohama stays rearward to prepared for tomorrow. By the period he gets home, it is already 11 p.m.

Ohama had continually dreamed of opening a café. When he established an established one listed on Relay, a Japanese business-succession platform, it felt akin fate.

The café's owners, an older brace looking to retire, no longer had the bodily stamina to run the business."Their biggest desire was merely for person to obtain fine attention of the shop and keep it a lively place," Ohama, 31, told Business Insider.

The former owners were getting on in age and wanted to discover a successor for their café. Provided by Kengo Ohama.

The conditions of the takeover were simple: He had to keep the endeavor operating, but was liberated to redesign the interiors as he saw fit. "That elasticity made the chance extremely attractive," Ohama said.

Ohama now covers 50,000 Japanese yen in monthly rent for the building. The endeavor itself — including the brand, furniture, galley equipment, recipes, and provider data — was transferred to him for zero Japanese yen.

Ohama stated the former owners wanted person passionate to win them, and were consenting to transfer the endeavor to him at no cost. Provided by Kengo Ohama.

A new generation steps in

Ohama is part of a younger generation taking complete Japan's small businesses as their longtime owners age out alongside no successor in sight.

The country's low commencement charge and quickly aging community have remaining many endeavor owners without grownup children to inherit their companies. Even whenever they do have kids, those heirs may live elsewhere, have established careers, or merely not desire to obtain on the demands of operating a local business.

In 2023, the Japan Finance Corporation established that 57.4% of small and midsize businesses it surveyed expected to near whenever their current owners stepped away, up from 52.6% in 2019.

That gap has created an beginning for platforms specified as Relay and BATONZ, which nexus owners looking to hand complete their businesses alongside prospective successors.

Relay has listed concerning 900 businesses to date, including approximately 350 energetic listings, a delegate told Business Insider. Owners looking to continue on their businesses are frequently in their 60s and 70s, during successors are frequently in their 30s and 40s.

BATONZ, which additionally handles additional accepted acquisitions by companies looking to expand, has listed additional than 42,000 businesses and matched 3,421 buyer-seller pairs as of March 2026, a spokesman told Business Insider.

Keeping a cherished location open

For several endeavor owners, these platforms can recommendation a lifeline whenever an unexpected emergency forces them to visage succession preparedness sooner than they expected.

Akitaka Mitsui opened Mori to Mizu Backpackers in 2011. This collage includes a photo of Mitsui and a photo he took of several guests who stayed at the guesthouse during his period as owner. Provided by Akitata Mitsui.

Akitaka Mitsui spent years operating as an outdoor tour guide before beginning Mori to Mizu Backpackers in Nagano, a city in chief Japan known for its alpine landscapes, in 2011.

When the epidemic brought journey to a standstill, he temporarily closed the guesthouse. Even following it was harmless to reopen, he had to measure rear the procedure and cut staff.

By 2022, visitant numbers were picking up again, and Mitsui was considering expanding formerly more. Then he suffered a stroke.

"It was not severe, but I had to be hospitalized," Mitsui, 67, told Business Insider. "The logic of the blow was never identified, which meant it could happen again. I began to think it would be improved to halt operating the business."

He turned to BATONZ to discover a successor exterior his family.

"I never intended to create my children obtain complete the business," he said. "I wanted them to do what they wanted to do alongside their own lives."

Enter Kota Fujisawa, a Nagano native who moved to Tokyo for university and afterward worked in consulting.

Fujisawa, 29, had been looking for a way to assistance revitalize his residence region. Along alongside two younker friends, he took complete the guesthouse in May 2024, although one of them has since remaining the business. They renamed it Local Knot Backpackers.

Kota Fujisawa took complete the guesthouse alongside his younker friends, and they renamed it Local Knot Backpackers. Provided by Kota Fujisawa.

He stated they paid small than 10 myriad Japanese yen for the business, which came alongside its website, booking systems, and staff. The construction was not part of the transfer, and he declined to disclose the monthly rent.

It is a drastic alter from his stable agency job, anywhere he worked on fair one part of larger projects. Now, as a small-business owner, Fujisawa has additional liberty to form the complete procedure and decide anywhere to obtain it next.

Fujisawa says he appreciates the chance to engage alongside guests from all complete Japan and the world. Provided by Kota Fujisawa.

"My former activity was chiefly desk-based, but I now expend much additional period on-site and have additional opportunities to communicate immediately alongside a broad range of tourists," Fujisawa told Business Insider.

Still, there is a balancing act: Taking complete an established endeavor method deciding what to maintain and what to rework.

"If we changed everything, it would rotate into a entirely distinct place," he said.

For Fujisawa, operating the guesthouse method honoring what longtime guests loved during making the changes needed to transport it forward. Provided by Kota Fujisawa.

Getting a foothold in a new industry

Beyond handing complete complete businesses, succession platforms can additionally assistance aspiring entrepreneurs acquire admission to the infrastructure they need to get started.

For Takayuki Otawara, a 29-year-old erstwhile civic servant in Miyazaki, it opened a way into agriculture notwithstanding his deficiency of agriculture experience. His involvement in cacao agriculture began during a stressful extend at work, whenever he discovered accomplishment chocolate and cut in affection alongside it.

"I had never farmed before," Otawara told Business Insider. "The lone item I had always grown was a small tomato factory for a primary-school project."

Takayuki Otawara wanted to commencement his own farm, notwithstanding having no agriculture experience. Provided by Takayuki Otawara.

His parents initially opposed his decision to depart a stable authorities job for farming. His friends, though, were additional encouraging. "They mostly idea it echoed interesting," he said.

To get started, Otawara needed a greenhouse and farmland, neither of which his family owned. After struggling to discover one through local farmers, he turned to Relay and established a listing from a strawberry-farming family looking to measure rear their operations as they got older.

Under a endeavor succession agreement, Otawara bought a greenhouse and irrigation equipment from the first owners for 3 myriad Japanese yen.

He used the accommodation to commencement his own farm, anywhere he now grows cacao alongside strawberries, bananas, rice, and another crops.

Otawara didn't obtain complete the business; instead, he used the accommodation to initiate his own farm. Provided by Takayuki Otawara.

He is at the ranch all day, anywhere his activity starts at 6 a.m. and frequently ends about 7 pm He is normally in bed by 10 p.m.

The activity is additional demanding than his erstwhile agency job, which came alongside weekends and community holidays off. However, agriculture has stated him a benevolent of fulfillment he never established as a civic servant.

"The activity I put in comes rear to me immediately as results," Otawara said. "In the government, you have a clearer idea of what your pay and occupation volition appearance akin until retirement. Now, how much I do and how much I acquire can change. I discover that breathtaking and rewarding."

This Box executive loves candidates with side projects. His own involves bringing back old-school TV.

support@mycoding.id (Aditi Bharade) — Thu, 22 Oct 2026 06:11:35 +0700

Rand Wacker, a Box executive, loves a fine flank project. Box

Knowing how to use AI is lone adequate to get a ft in the entrance for interviews, according to a Box executive.

Rand Wacker, Box's vice chairman of merchandise strategy, stated that for candidates applying for a job at Box, AI literacy is a bare-minimum requirement. Having a completely developed flank hustle or project — something he practices himself — is what gives possible hires a leg up in interviews.

Case studies, which he stated used to be a staple in Box interviews, are now a relic of the past. Now, he expects candidates to be prototyping out their ideas.

What he really likes to see is candidates showing him a merchandise they've built themselves using AI, equal if it has nothing to do alongside Box's business. He stated he's had possible hires display him mobile apps that they've built, gotten onto the App Store, and are monetizing to several degree.

"They're operating this small flank hustle overall," he said. "And it shows the passion."

He stated that group who examine and test alongside AI are the ones who win in the AI world, as they fig out how to use it most efficiently in their individual and expert lives.

He practices what he preaches

Wacker, who's spent nearly 30 years in the application engineering and merchandise administration fields, stated he started out as a "computer nerd" and loved playing about alongside application tools and construction things.

As he progressed in his career, he stated he had small period to test alongside building, but AI has lately allowed him to test small ideas he didn't have period to examine before vibe coding existed.

He lately made an app to bring rear old-school TV since he missed the days whenever he'd rotate it on, choice one of three cable channels, and it would perform a random movie from a random point.

"These days, whenever I'm trying to discover item to observe on the weekends, it's decision fatigue. Which of the six streaming services do I go to? What are the 20 myriad titles I scroll through?" he said.

Using Cursor, he built an Apple TV app that would admission his complete movie library, choice a random movie, and perform it from a random point.

Working on these kinds of projects is a awesome way to study the tools accessible on the market, get ideas for how to use them in another areas, and scratch the itch to create item of his own.

Wacker is one of many executives who have dipped their toes rear into construction and penning code in latest years. Leaders who haven't written lines of code in decades have started experimenting alongside AI coding to remain nearer to their products and awareness akin individual contributors.

Wait: Your autonomous Tesla won't drive everywhere just yet

support@mycoding.id (Lloyd Lee) — Thu, 22 Oct 2026 05:10:01 +0700

Tesla stated that it expects to publish unsupervised FSD about Q2 2027. Harald Tittel/picture alliance via Getty Images

When I reviewed Tesla's Full Self-Driving application in January, I hailed it as an "impressive" driver-assistance characteristic that remaining me wanting more.

I dreamed of being capable to flip the autonomous driving scheme on whenever I create my regular 400-mile trip from San Francisco to Los Angeles — boot my feet up, hear to music, and ideally autumn idle without having to supervise the road.

That's been part of Tesla's supreme throw for the future. "With autonomy, you get your period back," Elon Musk has said.

And the throw may shortly arrive true. Just don't anticipate to create lengthy self-driven highway trips fairly yet.

On Wednesday, during Tesla's Q3 earnings call, Ashok Elluswamy, the company's VP of AI, painted a additional humble image of what FSD unsupervised volition appearance akin for a individual car — at smallest at first.

Elluswamy stated unsupervised FSD is coming "somewhere about Q2 next year" for privately owned Teslas, alongside the rollout of FSD V15.

The caveat is that it volition lone be operable in areas anywhere Tesla already operates its Robotaxi service, which represents a small fraction of quadrate miles for the total figure of Teslas on US roads.

The Robotaxi platform is accessible in parts of seven metropolitan regions: Austin, Dallas, Houston, Miami, Orlando, Tampa, and the San Francisco Bay Area. In the Bay Area, all Tesla robotaxis are supervised by a individual operator.

"The lone additional necessity is to create certain that, for example, the client Model Ys can be part of the geofences and the fleet administration that Tesla has for it to be completely autonomous," Elluswamy said.

In another words, your Tesla may be capable to run itself, but lone if you're inside the zones anywhere Tesla Robotaxi is operating. If you're in Austin, that could shield concerning 260 quadrate miles of the city, excluding high-speed roadways.

That's akin to what another aspiring self-driving car companies have pitched.

Tensor, a San Jose-based automaker, aims to market a luxury sedan that it says can run itself. However, the self-driving characteristic volition lone run inside restricted geofences, the business told Business Insider.

Tesla didn't say why it needs to bounds the publish of unsupervised FSD to geofences at first, although there are apt questions concerning liability that the business volition have to location formerly it declares, Yes, our innovation now lets you obtain your eyes off the road.

Autonomy is a difficult issue to resolve following all. During the call, Elluswamy close downward the idea that competitors could capture up to Tesla's FSD.

"When they get in the lengthy rear of this issue — it's so lengthy that group don't acknowledge — anyone alongside a sane intellect should not attempt to resolve this problem," he said. "It's so difficult."

Sundar Pichai explains how a team of AI agents helps him run the company

support@mycoding.id (Katherine Li) — Thu, 22 Oct 2026 05:01:01 +0700

The Google CEO stated he uses AI agents to track Google's example betterment and inner discussions. Bloomberg/Getty Images

Even Google's CEO needs assistance keeping up alongside Google.

On the Sources podcast released Wednesday, Sundar Pichai described to presenter Alex Heath how he delegates several of the information-gathering that comes alongside operating the tech giant.

“It could effortlessly obtain me an hr a day to benevolent of capture up, what's happened in the final 24 hours, right?" Pichai said. "But now I have agents operating which summarize and bring that data to me. I can motionless go deeper, and I do so often."

Pichai stated that his AI agents provision him a way to "understand the example betterment arc," fig out how Google's computing resources are being used, and prosecute threads dispersed throughout many inner conversation groups without having to peruse through all conversation himself.

“Similarly, there are agents alongside telemetry into our compute use by merchandise area, efficiency, utilization, and giving me inputs and insights, you know," Pichai added. "I think it's made all of us, you know, be improved at what we do."

That doesn't average Pichai has handed complete the crucial calls to AI. He stated humans — including himself — remain immediately engaged in consequential decisions concerning allocating computing resources.

Managing agents is becoming a accomplishment in its own right, giving several workers a new set of headaches, from checking the bots' output to preventing them from duplicating activity or straying beyond their assignments. Business Insider earlier reported that the need to oversee agents is making everyone a boss without higher pay.

Delegating to bots doesn't necessarily create activity easier, either. A Cisco engineering director who manages additional than 10 agents told Business Insider that overseeing them involves additional project switching and can create activity awareness additional serious without shortening his days.

For the Google CEO, however, uninterrupted activity is a plus.

"So since your agents, they're operating in the cloud, they're operating 24/7, they have your context, and so they can choice up things anywhere you remaining off," Pichai said. "It's a continuum there. By the period it's doing that, you would be applying yourself to the next issue and so on."

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