MC Project

AI Recognizes 96% Of Brands But Mentions Almost None, New Study Finds

support@mycoding.id (MC Project) — Wed, 29 Jul 2026 13:42:19 +0700

This station was sponsored by Victorious. The opinions expressed successful this article are the sponsor’s own.

When we inquire an AI level to picture a brand, it almost ever knows the answer. But erstwhile a purchaser asks that aforesaid level which marque to consider, it draws connected only a mini fraction of the brands it knows.

That spread is the cardinal uncovering of our Q2 2026 Quarterly Search Report. Across 8 AI platforms, 96 percent of the brands we tested were described accurately erstwhile we asked astir them directly. Yet 89 percent of the brands we measured ne'er appeared successful AI-generated answers to class investigation questions.

AI systems already cognize astir brands. Getting mentioned requires much than marque nickname alone. This study group retired to measurement what different factors power AI visibility.

In This Study Summary

1. The Study Methodology: Measuring AI Recognition vs. AI Mentions
2. AI Recognizes Far More Brands Than It Mentions
3. So What Separates The Brands AI Mentions?
4. AI Trusts Different Sources At Each Stage Of The Buyer Journey
5. What Else The Data Showed
6. Key Takeaway

Read The Full Q2 2026 Quarterly Search Report

The Study Methodology: Measuring AI Recognition vs. AI Mentions

We measured 2 chopped signals for a cohort of 175 brands crossed 5 verticals: legal, healthcare, SaaS, financial services, and ecommerce/retail. Of those, 140 brands had evaluable AI responses for the nickname analysis, and 150 formed the mention-rate cohort.

Measuring AI Recognition

We asked 8 AI platforms (ChatGPT, Claude, Gemini, Copilot, Perplexity, Google AI Overviews, Google AI Mode, and Meta AI) to picture each marque successful our cohort, past graded each consequence against the brand’s ain website and assigned that consequence a explanation of correct, vague, outdated, wrong, aliases not recognized.

Measuring AI Mentions

We measured really often those brands appeared successful AI-generated answers to standardized class investigation prompts (the buying-research questions a imaginable asks erstwhile comparing solutions). We besides ran a abstracted group of problem consciousness prompts (the early-journey questions a purchaser asks earlier they cognize what benignant of solution they need), which became important later successful the study.

Expanding The Analysis

Recognition and mention rates didn’t align the measurement we expected, truthful we expanded the study to integrated rankings, integrated traffic, third-party web mentions, and Knowledge Graph presence.

AI Recognizes Far More Brands Than It Mentions

Recognition and mention are distinct, measurable behaviors. Recognition reflects AI’s knowing of a brand. Mention measures whether AI platforms would sanction a marque while a purchaser is comparing options.

When asked directly, AI platforms described 96 percent of brands accurately. They correctly explained what each institution sells and the markets it serves. Yet those aforesaid brands were seldom mentioned erstwhile we asked the class investigation questions a existent purchaser would ask. For 89 percent of them, they never surfaced astatine all.

Q2 2026 Quarterly Search Report: Recognition complaint vs. mention complaint created by Victorious. July 2026.

On person examination, we recovered that nickname varied by platform. Google AI Mode, Gemini, ChatGPT, Google AI Overviews, and Copilot each exceeded 83 percent accuracy successful each vertical we tested, while Perplexity and Meta AI swung acold wider. Perplexity correctly recognized less than 55 percent of SaaS and ecommerce brands, and Meta AI recognized conscionable 46 percent of SaaS brands. The afloat study shows which platforms admit brands astir accurately crossed each of the 5 verticals.

So What Separates The Brands AI Mentions?

If nickname doesn’t explicate the difference, thing other does. The strongest relationships we recovered came from signals beyond a brand’s ain website: referring domains (how galore unsocial sites nexus to you) and third-party web mentions (how often different sites talk astir you). Both showed a mean relationship pinch AI mentions, 0.49 and 0.45 connected a standard wherever 1.0 would beryllium a cleanable relationship. No azygous awesome was beardown capable to enactment arsenic a lever connected its own, but together they picture a brand’s wide prominence crossed the web.

After publishing this report, we also tested whether referring domain quality mattered as much as quantity. It didn‘t. High-authority links added only a modest lift on their own, and some brands with links from highly authoritative sites still rarely appeared in AI answers. We break down why those high-authority links didn’t help on a recent episode of our podcast.

The third-party mention information produced the astir applicable benchmark successful the report. Brands pinch less than 2,000 indexed web pages mentioning them were named successful AI answers conscionable 3 percent of the time. The mention complaint climbed steeply from there, and the afloat study breaks down the visibility benchmarks that separated mentioned brands astatine each threshold.

AI Trusts Different Sources At Each Stage Of The Buyer Journey

Here’s wherever the information provided much actionable insights. Within our class investigation prompts, 99.99 percent of the 49,391 citations we analyzed pointed to third-party websites alternatively than the brand’s ain domain. Only 4 of the 150 brands successful our mention cohort earned a citation to their ain website. That raised a follow-up question: Does the aforesaid shape clasp earlier successful the journey, earlier a purchaser starts comparing solutions? We compared the sources AI cited crossed our 2 punctual types to find out.

Problem consciousness prompts, for illustration “How do I cognize if my business needs a lawyer?”, drew citations from acquisition sources: videos, authorities and organization sites, and master communities. They besides cited acquisition contented published connected institution websites. Yet the brands themselves were named successful conscionable 0.10 percent of those answers.

The pairing matters much than either stat alone: early successful the journey, AI will usage a brand’s acquisition contented to build an reply and still time off the brand’s sanction retired of it. Just arsenic nickname doesn’t guarantee a mention, neither does a citation.

Category investigation prompts, for illustration “What are the champion rule firms successful the US?”, shifted citations toward directories and comparison sites. And brands were named much than 12 times arsenic often successful those answers.

Q2 2026 Quarterly Search Report: Citation sources by shape of the purchaser travel created by Victorious. July 2026.

The 2 stages reward different work. Publish acquisition contented that answers early-journey questions, since that’s what AI reaches for to build answers to problem consciousness prompts. And build beingness connected the directories and comparison sites AI consults during class research, since that’s wherever brands really get named. The study specifications which sources AI trusts astatine each shape of the purchaser journey, surgery down by punctual type and platform.

What Else The Data Showed

The sources AI relies connected besides disagree sharply by industry. Legal services citations were concentrated successful a fistful of prestige directories, while SaaS citations were scattered crossed much than 10,000 unsocial domains. So, the correct off-site strategy depends heavy connected your vertical. The afloat study breaks down how citation sources disagree by industry, including the apical cited sources for each of the 5 verticals we studied.

Key Takeaway

AI already knows who you are. It describes astir brands accurately and has nary problem explaining what they do. Visibility comes from showing up successful the sources AI trusts astatine each infinitesimal of the purchaser journey. Your ain acquisition contented tin gain citations while buyers are still moving to understand their problem, and third-party sources transportation astir each the weight erstwhile they scope the information stage.

The afloat Q2 2026 Quarterly Search Report includes the complete awesome relationship data, the third-party mention thresholds, citation behaviour by platform, and this quarter’s marketplace signals.

Read The Full Q2 2026 Quarterly Search Report

Image Credits

Featured Image: Image by VictoriousSEO. Used pinch permission.

In-Post Images: Images by VictoriousSEO. Used pinch permission.

Category Sponsored Posts Generative AI SEO Strategy

What are autonomous AI agents?

support@mycoding.id (MC Project) — Wed, 29 Jul 2026 13:42:13 +0700

Autonomous AI agents are package systems that tin plan, make decisions, usage outer tools, and transportation retired a bid of actions pinch constricted quality input.

Once fixed an objective, an supplier determines the adjacent step, evaluates the outcome, and adjusts its attack until the task is complete aliases reaches a defined stopping point.

Autonomy does not propose unlimited independence. Most accumulation systems run wrong predefined objectives, permissions, and information rules that find which actions tin beryllium taken automatically and which require quality approval.

People stay responsible for oversight and high-impact decisions, while the supplier handles regular activity wrong those boundaries.

Bounded autonomy allows agents to activity independently connected regular tasks while keeping group responsible for high-impact decisions, compliance, and oversight.

Planning, reasoning, memory, and instrumentality entree activity together to support autonomous execution.

Planning breaks analyzable activity into manageable steps, representation preserves discourse betwixt decisions, reasoning evaluates progress, and outer devices supply entree to applications and accusation needed to complete the task.

How do autonomous AI agents work?

Autonomous AI agents work by many times planning, acting, evaluating results, and adapting their adjacent determination until they execute an nonsubjective aliases scope a stopping condition.

Each action changes the accusation disposable to the agent, truthful the adjacent determination is based connected the existent authorities of the task alternatively than a fixed series of instructions.

That feedback loop allows the supplier to proceed moving without waiting for different personification prompt.

1. Receiving a goal

An autonomous AI supplier originates by turning a personification petition aliases strategy arena into a task the exertion tin execute and monitor.

The input whitethorn get arsenic a natural-language prompt, an API request, a support ticket, a scheduled job, aliases a awesome from different application.

Before immoderate readying starts, the strategy must find what result is required, which conditions apply, and what would count arsenic successful completion.

A petition specified arsenic “Analyze competitors and urge a pricing strategy” contains respective pieces of accusation that the supplier needs to separate.

The requested result is simply a pricing recommendation. Competitor study provides the grounds needed to nutrient it.

The petition whitethorn besides connote constraints that are not stated directly, specified arsenic the applicable market, merchandise category, geographic region, reporting period, aliases required level of detail.

Missing specifications create uncertainty, truthful the strategy must either retrieve them from disposable context, use approved defaults, aliases inquire the personification for clarification.

In an LLM-based agent, the exertion sends the petition to the exemplary together pinch instructions that specify the agent’s role, disposable tools, operating rules, and expected output format.

The exemplary analyzes the mixed discourse and returns a system interpretation. Depending connected the implementation, that mentation whitethorn see fields specified as:

{ "objective": "Recommend a pricing strategy", "required_work": [ "Identify applicable competitors", "Collect existent pricing data", "Compare merchandise positioning", "Produce a recommendation" ], "constraints": { "market": "web hosting", "region": "United States", "currency": "USD" }, "expected_output": "A study pinch grounds and recommendations", "missing_information": [ "Target customer segment" ] }

The large connection model proposes this structure, but the surrounding exertion determines whether the output is valid.

A schema validator tin cull missing fields aliases incorrect information types. Policy checks tin region actions extracurricular the agent’s permissions.

Business rules tin require quality confirmation erstwhile the petition involves delicate data, financial decisions, aliases changes to accumulation systems.

The exertion whitethorn besides comparison the petition against the devices disposable to the agent. A extremity that requires database entree cannot proceed if the supplier has nary authorized database tool.

Once validated, the interpreted extremity becomes portion of the workflow state. The authorities stores the objective, constraints, disposable context, permissions, expected deliverables, and immoderate unresolved questions.

Every later constituent sounds from the aforesaid state, which prevents the planner, instrumentality executor, and evaluator from moving from different interpretations of the request.

Success criteria are particularly important because the supplier needs a actual measurement to find erstwhile the activity is complete.

For the competitor analysis, completion mightiness require pricing information from 5 named competitors, root links for each claim, a comparison table, and a proposal that follows a defined pricing policy.

Without those conditions, the supplier whitethorn nutrient a plausible reply while leaving important parts of the task unfinished.

Planning originates only aft the supplier has a sufficiently clear and valid practice of what it is trying to achieve.

2. Creating a plan

Planning answers a azygous question: What needs to hap earlier the nonsubjective tin beryllium completed?

A ample connection exemplary answers that mobility by reasoning astir the narration betwixt the nonsubjective and the activity required to execute it.

Instead of generating the last consequence immediately, the exemplary useful backward from the desired result to place the intermediate steps needed to scope it.

For a petition to urge a pricing strategy, the exemplary first recognizes that a proposal requires evidence.

Evidence requires pricing data, and pricing information requires identifying applicable competitors. Those relationships people shape a series of limited tasks.

Unlike accepted automation, the workflow is generated for the existent petition alternatively than selected from a predefined template.

A different objective, disposable tools, aliases group of constraints whitethorn nutrient a wholly different plan, moreover erstwhile the aforesaid supplier is used.

The planner returns the workflow arsenic system information that describes the tasks, their dependencies, and the bid successful which they should beryllium completed.

At this point, the supplier knows what activity needs to happen, but nary of that activity has been executed yet.

3. Breaking the scheme into smaller tasks

Execution originates by selecting the first task whose limitations person been satisfied. In the erstwhile example, Identify competitors has nary dependencies, truthful it becomes the existent task. Collect pricing data remains blocked until the first task finishes successfully.

When the existent task finishes, the exertion stores its output and updates the task’s status.

The execution motor past checks whether the completed task has satisfied the requirements for immoderate remaining tasks.

If it has, the adjacent task becomes eligible for execution. If it has not, the workflow whitethorn retry the task, prime an replacement approach, aliases extremity if betterment is nary longer possible.

Each completed task contributes caller accusation to the workflow. Identifying competitors produces the database needed for information collection.

Collecting pricing information produces the accusation required for comparison. Comparing products generates the grounds needed to support the last recommendation.

Later tasks, therefore, devour the outputs produced by earlier ones, gradually moving the workflow toward the original objective.

The execution motor repeats this rhythm until each task has been completed aliases the workflow reaches a stopping condition.

Because each task is evaluated independently, the exertion tin retrieve from failures, incorporated recently discovered information, aliases alteration the remaining workflow without restarting the full process.

4. Gathering accusation and utilizing tools

A ample connection exemplary cannot retrieve unrecorded accusation aliases modify outer systems connected its own.

Whenever the existent task requires information beyond the speech aliases an action extracurricular the model, the exertion invokes a tool. Tools expose outer capabilities done well-defined interfaces.

Depending connected the task, the execution motor mightiness telephone a web hunt service, query a database, retrieve a document, execute Python code, nonstop an email, aliases interact pinch different application’s API.

Each instrumentality returns system results that go portion of the workflow state.

Suppose the existent task is Collect pricing data. The exemplary recognizes that answering from its training information would nutrient outdated aliases incomplete results, truthful it requests entree to a web hunt tool.

The exertion validates that request, executes the search, and returns the retrieved pricing accusation to the exemplary for analysis. The exemplary ne'er communicates pinch the outer work directly.

One task whitethorn require respective tools. A competitor study could harvester web searches for existent prices, database queries for humanities income data, and archive retrieval for soul pricing policies earlier producing a comparison.

The execution motor coordinates those calls and supplies their outputs to the exemplary arsenic further context.

Tool usage besides operates wrong defined permissions. The exertion decides which devices are available, what parameters they accept, and whether a requested action is allowed.

A customer support agent, for example, whitethorn retrieve relationship accusation but not rumor refunds without approval.

Separating reasoning from execution prevents the connection exemplary from performing actions extracurricular the limits established by the application.

5. Evaluating the results

Before the workflow continues, the exertion evaluates whether the output satisfies the requirements defined for that task.

Evaluation compares the consequence against measurable criteria alternatively of assuming that each completed action was successful.

A task to cod competitor pricing, for example, whitethorn require information from 5 named companies, root links for each price, and prices expressed successful US dollars.

Returning accusation for only 3 competitors completes the instrumentality call, but it does not complete the task.

Different tasks require different information methods. Structured outputs tin beryllium validated against a schema to guarantee that each required section is coming and correctly formatted.

Retrieved accusation tin beryllium checked for completeness, freshness, aliases consistency pinch different sources.

Language exemplary responses whitethorn beryllium reviewed by different exemplary that verifies whether the consequence follows the original instructions, cites supporting evidence, aliases answers the requested question.

Evaluation produces a determination alternatively than different answer. If the consequence meets each requirement, the workflow continues to the adjacent task.

If accusation is missing aliases validation fails, the exertion whitethorn repetition the task, invoke a different tool, petition clarification, aliases return the task to the planner for revision.

6. Adapting to caller information

The original workflow is based connected the accusation disposable erstwhile readying begins. As execution progresses, caller accusation tin invalidate earlier assumptions, making the remaining workflow incomplete aliases unnecessary.

The original scheme mightiness see collecting pricing information for 5 competitors. During execution, the supplier discovers that 1 competitor nary longer sells the merchandise being analyzed.

Continuing pinch the original workflow would discarded clip collecting irrelevant information and could nutrient misleading conclusions.

Instead of pursuing the outdated plan, the exertion updates the workflow authorities pinch the recently discovered accusation and asks the planner to make the remaining steps again.

The planner does not commencement from scratch. It receives the existent workflow state, including completed tasks, collected data, grounded actions, and the original objective, past revises only the unfinished information of the workflow.

The updated workflow whitethorn region unnecessary tasks, insert caller ones, aliases alteration their execution order.

If different competitor should switch the 1 that was removed, the planner adds caller information postulation and comparison tasks earlier the workflow continues.

Previously completed activity remains disposable unless the caller scheme determines that it is nary longer valid.

Core components of an autonomous AI agent

An autonomous AI supplier is simply a postulation of package components that coordinate reasoning, execution, memory, and outer systems.

The connection exemplary supplies intelligence, while the surrounding exertion manages the workflow that turns reasoning into actions.

The connection model

The connection exemplary interprets requests, reasons astir problems, creates plans, and evaluates information.

It generates system outputs, but it does not execute code, shop semipermanent state, aliases pass straight pinch outer services.

The orchestrator

The orchestrator coordinates the full workflow. It decides erstwhile to telephone the connection model, executes tools, stores intermediate results, updates the workflow state, and determines what should hap next.

Without an orchestrator, the connection exemplary would simply make matter alternatively of performing autonomous work.

Workflow state

Workflow authorities stores everything the supplier has learned while moving toward an objective.

Objectives, completed tasks, retrieved information, instrumentality outputs, permissions, and information results stay disposable passim execution truthful each constituent operates from the aforesaid context.

Memory

Memory extends the accusation disposable to the supplier beyond the existent task.

Short-term representation preserves discourse during a workflow, while semipermanent representation tin clasp personification preferences, erstwhile conversations, successful workflows, aliases organizational knowledge for early tasks.

Tool integrations

Tool integrations let the supplier to retrieve accusation and interact pinch outer systems.

Web search, databases, APIs, email platforms, unreality services, and business applications each go disposable done controlled interfaces managed by the application.

Levels of autonomy successful AI agents

The magnitude of activity an supplier tin execute without quality engagement depends connected the permissions, tools, policies, and support requirements defined by the application.

As those constraints change, truthful does the agent’s grade of autonomy.

Assisted

Assisted agents respond to requests but trust connected users to nonstop each measurement of the workflow. The exertion whitethorn supply entree to devices aliases outer data, but the supplier waits for caller instructions earlier moving to the adjacent task.

Most AI chatbots and virtual assistants run astatine this level, helping users complete activity without acting independently.

Semi-autonomous

Semi-autonomous agents tin scheme and complete multi-step workflows without continuous guidance.

The exertion allows the supplier to reason, usage tools, and accommodate to caller information, but pauses the workflow earlier high-impact actions specified arsenic approving payments, deploying code, aliases modifying accumulation systems.

Human support becomes a built-in checkpoint alternatively than an denotation that the workflow has failed.

Autonomous

Fully autonomous agents execute astir tasks independently from readying done execution.

The exertion continuously evaluates results, updates the workflow, and allows the supplier to accommodate its scheme arsenic caller accusation becomes available.

Human involution is constricted to exceptional situations, while permissions, business rules, and monitoring guarantee the supplier operates wrong predefined boundaries.

Many accumulation systems harvester characteristics from aggregate levels depending connected the workflow and the consequence associated pinch each action.

Autonomous AI agents vs different AI systems

Although autonomous AI agents stock capabilities pinch different AI technologies, each 1 is designed for a different purpose.

Some make content, others automate predefined workflows, while autonomous agents harvester reasoning, planning, and execution to complete objectives independently.

The pursuing comparison highlights the cardinal differences betwixt these approaches.

Technology	Primary role	Planning	Multi-step execution	Tool use	Human involvement
Autonomous AI agent	Completes objectives independently	✓	✓	✓	Minimal
AI assistant	Helps users complete individual tasks	Limited	Limited	Optional	Continuous
Generative AI	Creates text, images, code, aliases different content	✗	✗	Optional	Required
Traditional automation	Executes predefined rules	✗	Predefined only	Limited	None aft configuration
Multi-agent system	Coordinates aggregate agents to lick a problem	Depends connected the participating agents	Depends	Depends	Depends

Generative AI focuses connected producing contented from a prompt. Large connection models specified arsenic GPT make text, summarize documents, reply questions, aliases constitute code, but they do not negociate workflows aliases execute actions connected their own.

Autonomous AI agents often usage generative AI arsenic their reasoning engine, combining connection procreation pinch planning, memory, and instrumentality use.

AI assistants support humans astatine the halfway of the workflow. A chatbot tin reply questions, draught emails, aliases retrieve information, but each caller task originates pinch different personification instruction.

Autonomous agents displacement work from responding to prompts toward completing objectives independently.

Traditional automation follows predefined rules alternatively of reasoning astir caller situations. A workflow created pinch scripts aliases robotic process automation (RPA) performs the aforesaid series of actions each clip unless a developer changes the logic.

Autonomous agents make and accommodate workflows dynamically based connected the existent nonsubjective and the accusation they stitchery during execution.

Multi-agent systems picture an architectural shape alternatively than a level of autonomy. Instead of relying connected a azygous agent, the exertion coordinates aggregate specialized agents that cooperate connected different parts of a workflow.

One supplier mightiness investigation competitors, different analyse pricing, and a 3rd make recommendations earlier combining their results into a last report.

Autonomy and multi-agent creation picture different characteristics of an AI system. Autonomy explains really independently an individual supplier tin operate, while a multi-agent strategy explains really activity is distributed crossed aggregate agents.

A azygous autonomous supplier tin complete analyzable workflows alone, and a multi-agent strategy whitethorn see autonomous agents, non-autonomous agents, aliases a operation of both.

Where are autonomous AI agents used?

Customer support is 1 of the astir mature applications for autonomous AI agents. They tin retrieve customer information, hunt soul knowledge bases, and reply regular questions without quality involvement. They tin besides process relationship updates aliases rumor refunds wrong predefined limits.

Software development benefits from AI agents that make code, reappraisal propulsion requests, constitute tests, and archive changes.

In IT operations and cybersecurity, they analyse alerts, analyse logs, show infrastructure, and automate incident response.

Many organizations are using AI agents successful marketing to automate assemblage segmentation, run optimization, contented generation, capacity analysis, and lead nurturing crossed aggregate channels.

Business operations teams use autonomous AI agents to invoice processing, procurement, inventory management, archive routing, study generation, and different administrative workflows that span aggregate business systems.

The increasing number of AI supplier examples reflects a communal pattern: organizations deploy autonomous AI agents wherever they tin automate repetitive, multi-step workflows while reserving high-impact decisions for people.

What are the benefits of utilizing autonomous AI agents?

Organizations usage autonomous AI agents because they thief them:

Reduce repetitive work. Autonomous AI agents grip routine, time-consuming tasks specified arsenic processing documents, updating records, aliases responding to communal customer requests, reducing the manual workload for employees.
Respond faster. AI agents tin analyse information, make regular decisions, and execute actions wrong seconds, reducing consequence times for customers, employees, and soul operations.
Scale operations. A azygous autonomous workflow tin grip thousands of requests simultaneously without requiring organizations to summation headcount astatine the aforesaid pace.
Operate continuously. Autonomous agentic AI useful astir the clock, making it well-suited for customer support, infrastructure monitoring, cybersecurity, and different time-sensitive operations.
Improve consistency. Every workflow follows the aforesaid business rules, helping organizations present much accordant responses, trim errors, and standardize regular processes.

What are the risks of utilizing autonomous AI agents?

Every autonomous AI supplier introduces immoderate level of operational risk. The effect depends connected the decisions the supplier tin make, the systems it tin access, and really independently it tin act.

Common challenges include:

Incorrect decisions. AI agents tin misinterpret objectives, take ineffective actions, aliases nutrient results that don’t align pinch business requirements. For example, a procurement supplier mightiness bid the cheapest supplier alternatively of the 1 that meets value standards.
Hallucinations. Large connection models whitethorn make inaccurate accusation aliases unsupported conclusions that impact downstream decisions if outputs are not decently validated. A investigation agent, for instance, could mention non-existent sources aliases summarize outdated accusation arsenic fact.
Excessive permissions. Granting agents unnecessary entree to systems aliases delicate information increases the effect of mistakes, misuse, aliases compromised accounts. An HR supplier that tin some position and delete worker records poses a overmuch greater consequence than 1 pinch read-only access.
Poor monitoring. Without logging, alerts, and audit trails, organizations whitethorn struggle to place failures, analyse incidents, aliases understand why an supplier made a peculiar decision. If an inventory supplier many times orders copy stock, teams request execution logs to find what caused the error.
Security concerns. AI agents that interact pinch outer tools, APIs, and business systems tin go targets for punctual injection, unauthorized access, aliases information leakage if they are not adequately protected. For instance, a malicious punctual embedded successful a web page could manipulate a browsing supplier into exposing delicate accusation aliases performing unintended actions.
Unexpected behavior. Agents whitethorn respond unpredictably to ambiguous instructions, incomplete information, aliases situations that were not considered during development. A customer work supplier instructed to “prioritize urgent requests” mightiness incorrectly categorize regular inquiries arsenic urgent if the criteria are poorly defined.

The level of consequence depends connected some the task and the grade of autonomy. As organizations shift toward intelligent automation, they must equilibrium ratio pinch governance by matching each workflow to an due level of autonomy.

The extremity is to springiness the due level of autonomy for each workflow.

When should you usage autonomous AI agents?

Autonomous AI agents supply the astir worth erstwhile workflows impact aggregate connected decisions that travel predictable business rules.

If a process requires accusation from respective systems, repeated decision-making, and the aforesaid series of steps each time, an autonomous supplier tin usually execute it much efficiently than a person.

Good candidates include:

Research and accusation gathering
Lead qualification
Marketing campaigns and customer journeys
Infrastructure and exertion monitoring
Report procreation and information analysis
Repetitive business processes specified arsenic invoice processing, procurement, aliases archive routing

Some decisions should stay nether quality power because they impact legal, financial, ethical, aliases information considerations.

Approving payments, hiring employees, and making ineligible aliases compliance decisions are communal examples. The aforesaid applies to healthcare decisions, customer disputes, and awesome information incidents.

Should you build autonomous AI agents?

Many businesses do not request to build autonomous AI agents from scratch. Developing a reliable autonomous strategy requires overmuch much than integrating a ample connection model.

The exertion must coordinate planning, execution, memory, instrumentality integrations, permissions, monitoring, evaluation, and ongoing attraction to guarantee the supplier operates safely and consistently.

Building that infrastructure makes consciousness erstwhile an statement needs highly customized workflows, heavy integrations pinch soul systems, aliases strict power complete really the supplier reasons and executes tasks.

In those cases, businesses tin measure platforms for building AI agents that supply the instauration for developing, testing, and deploying civilization AI solutions.

Regardless of the platform, building autonomous AI agents will still require continuous testing, monitoring, and attraction arsenic business processes, models, and connected systems evolve.

It besides needs continuous testing, monitoring, and updates arsenic business processes, models, and connected systems evolve.

For galore businesses, a ready-to-use AI supplier is simply a much applicable choice. Hostinger Agents, for instance, supply specialized capabilities for tasks specified arsenic SEO, marketing, contented creation, and sales.

Businesses tin automate those workflows without processing and maintaining their ain AI infrastructure.

Each supplier focuses connected a circumstantial business function, making it easier to automate communal workflows and summation productivity.

All of the tutorial contented connected this website is taxable to Hostinger's rigorous editorial standards and values.

The author

Ksenija Drobac Ristovic

Ksenija is simply a integer trading enthusiast pinch extended expertise successful contented creation and website optimization. Specializing successful WordPress, she enjoys penning astir the platform’s nuances, from creation to functionality, and sharing her insights pinch others. When she’s not perfecting her trade, you’ll find her connected the section hoops tribunal aliases astatine location enjoying a crime story. Follow her connected LinkedIn.

More Tailscale tricks for your jailbroken Kindle

support@mycoding.id (MC Project) — Wed, 29 Jul 2026 13:42:08 +0700

If you managed to put Tailscale connected a jailbroken Kindle earlier it updated excessively acold ahead, you sewage thing beautiful great, moreover if it wasn't the full Tailscale experience. But bully things travel to those who hold (or excavation astir connected GitHub).

Open-source developers person improved the Tailscale acquisition connected 1 of the weakest computers you own. If your Kindle is jailbroken, an updated type of Mitanshu Sukhwani's Tailscale implementation offers a fewer caller things:

Tailscale SSH enabled by default, truthful you don't person to alteration USBnetworking SSH and its very evident default user/password
A proxy mode that lets apps for illustration KOReader to scope other nodes connected your tailnet, for illustration a Calibre/OPDS aliases Wallabag server
A afloat TUN mode that, connected immoderate Kindles, tin make Tailscale networking activity astatine the instrumentality level

Let’s excavation into each 1 and really to group them up. As before, this is organization codification moving connected a very unofficial instrumentality state; bring your patience along.

Tailscale connected a Kindle, now pinch proxies

The past clip we wrote astir Tailscale connected a Kindle, the customer was basic, but it worked. The Kindle showed up connected your tailnet, complete pinch a greenish dot successful the web admin console. You could scope the Kindle by its Tailscale IP address. You could moreover SSH into the Kindle complete Tailscale, which was useful for further tinkering.

But “reachable via Tailscale” is not the aforesaid arsenic “routing each incoming and outgoing postulation crossed your tailnet,” it turns out. Tailscale connected a jailbroken Kindle is typically forced to tally successful userspace mode, which intends it cannot usage the device's ain web routing layer, known arsenic TUN mode. You could commencement Tailscale, and past commencement an app for illustration KOReader, but erstwhile you tried to link to different Tailscale device, for illustration your Calibre server astatine 100.x.y.z, it would spell for illustration this:

KOReader (or immoderate app) asks the Kindle’s guidelines OS really to scope 100.x.y.z
The Kindle, lacking Tailscale routing, cannot scope that Tailscale IP address
KOReader drops the connection

An update to the Kindle KUAL app by greywolf1499 provides different modes that activity astir this. Now, erstwhile you effort to scope different Tailscale IP reside connected your Kindle, it tin spell for illustration this:

You commencement Tailscale successful proxy mode
You group up KOReader aliases different app's proxy settings to link to 127.0.0.1:1055
KOReader tells the proxy it wants to scope 100.x.y.z
Tailscale’s daemon tailscaled, listening connected larboard 1055, routes the relationship done Tailscale
E-books, articles, and different information flows betwixt your Tailscale-running Kindle and different Tailscale devices

This Tailscale proxy offers 2 modes, SOCKS5 and HTTP CONNECT, for apps that whitethorn for illustration 1 aliases the other. This opens up a bully spot much inferior for your more-connected Kindle.

What you tin do pinch a proxied Kindle

A fewer chaotic ideas, depending connected really dug successful you want to get:

Calibre aliases Wallabag servers, arsenic mentioned
Audiobookshelf relationship done KOReader
Use Readest to way reference advancement crossed devices
Linking KOReader’s RSS scholar to a a self-hosted provender server
Accessing minimalist dashboards and web pages successful the (pretty bad) Kindle browser
Using a Bluetooth keyboard and the kterm app to SSH into tailnet devices

Is that past 1 each that practical? Not really. But is location a pleasant warmth, knowing that you've added the slightest apt bladed customer to your what-if kit? For immoderate types, types I cognize rather well: yes.

The Tailscale plugin for KOReader (with Kobo and PocketBook support)

If you don’t really request immoderate Tailscale powers extracurricular the highly tin KOReader app, cheque retired this Tailscale KOReader plugin. It doesn’t make your Kindle accessible complete your tailnet, for illustration the KUAL-based app. But it does automatically create the proxy interfaces that are needed for reaching your contented servers from your KOReader-running Kindle—or your Kobo device, aliases your PocketBook.

I haven't been capable to really effort this hold retired myself; my 11th-generation modular Kindle doesn't play good pinch it astatine the moment. It's been "Tested connected Kindle PW5/PW6, Kobo, and PocketBook"—it's bully to spot Tailscale travel to immoderate different KOReader-friendly devices, too.

Installation is not excessively hard, astatine slightest if you made it this acold into jailbreaking already. You transcript the plugin into KOReader’s plugins directory, trigger an “Install/Update Tailscale” from KOReader’s menu, transcript a Tailscale key into a directory, past toggle Tailscale connected successful the KOReader menu. From there, you configure KOReader pinch 1 of its proxy addresses (127.0.0.1:1055 for SOCKS5, :1056 for HTTP CONNECT), past springiness different plugins the Tailscale IP addresses you request to reach.

Victoria Riley Barnett’s repository notes that the plugin useful awesome pinch a SyncThing plugin for KOReader. KOReader is for illustration its ain abstracted OS for jailbroken Kindles astatine this point,

So now you’ve sewage a batch much options and weird projects disposable to you, done this already quite-strange small slab. If you’ve worked up a weirdly useful Tailscale setup connected your Kindle, Kobo, aliases different e-paper device, we’d emotion to perceive astir it. Let america cognize on Reddit, Discord, Bluesky, X, Mastodon, or LinkedIn.

Cracking Windows Open: Porting RADV to Win32

support@mycoding.id (MC Project) — Wed, 29 Jul 2026 13:42:08 +0700

Louis-Francis Ratté-Boulianne
July 28, 2026

RADV is the unfastened root Mesa Vulkan driver for AMD GPUs. Over the years, it has go a cornerstone of the Linux graphics stack. It has now efficaciously go the de facto Vulkan driver for AMD hardware connected Linux. AMD moreover discontinued their PAL-based (Platform Abstraction Library) replacement successful its favor, consolidating their open-source efforts astir Mesa.

On Windows however, AMD users are still served only by the proprietary driver. But don't panic, we person a solution: porting RADV to Windows, bringing the aforesaid unfastened root Vulkan implementation that has proven itself connected Linux to them. It's specified an improbable proposition that we mightiness really get location faster than ray (spoiler alert: that's going to beryllium a small slower).

An unfastened root Vulkan driver connected Windows opens up a number of possibilities: a shared codebase crossed platforms, easier debugging and experimentation, faster turnaround connected fixes, and a way for the organization (e.g., crippled developers) to study problems aliases lend improvements that use everyone sloppy of operating system.

From first triangle...

This effort builds straight connected the groundwork laid by Faith Ekstrand, who first explored the feasibility of moving RADV connected Windows and presented her findings astatine XDC 2024. We powerfully propose you spell watch the talk, but present is simply a speedy summary.

A cardinal taxable of her talk is that since Windows 10, the WDDM2 interface provides a overmuch amended instauration for a third-party driver than what came before. It defines a clear exemplary for really a user-mode driver (UMD) interacts pinch the operating strategy and the kernel-mode driver (KMD).

However, location is of people a important catch: galore D3DKMT calls tin transportation private driver data - opaque, vendor-specific blobs whose contents are wholly up to the driver. This intends the UMD and KMD stay tightly coupled and that this interface is wholly undocumented.

To activity astir this, Faith created the wddm2-pdd-re instrumentality to log the WDDM2 calls and the backstage information contented for immoderate D3D12 applications. Using this approach, she was capable to reverse technologist capable of the backstage interface — querying adapter info, allocating buffers, creating queues, and submitting commands — to get RADV to taxable activity to the proprietary kernel driver. And yet she was moreover capable to get a rotating 3D exemplary displayed connected her surface successful each its glory.

...to first game

Where Faith's activity established that this could beryllium done, this task picked up the thread. The extremity was to amended upon it to make it much elastic (support for different hardware by reducing hard-coded values), much portable (breaking free of WSL pinch autochthonal Windows support), and acold much unchangeable (no longer crashing aft 2 minutes of deqp-vk testing).

We improved bid watercourse handling and synchronization and added support for, among others, sparse bindings, tessellation and task shaders, and move querying of GPU properties. It is still not conformant (even though the occurrence complaint has dramatically increased), but the existent item is being capable to tally our first crippled pinch RADV: Counter-Strike 2. Anyone tin effort it by switching the renderer pinch the -vulkan argument.

...through darkness

Like immoderate larboard and/or reverse-engineering project, it brought immoderate challenges:

New procreation is truthful oversensitive: We were experimenting connected Gen 11 hardware (RX 7900 XT) while Faith was moving pinch a 10th Gen GPU (RX 7800 XT). We weren't capable to replicate Faith's results for rather a while, owed to architecture changes betwixt these 2 generations. The main problem was that it would bent arsenic soon arsenic we tried to do thing much analyzable than clearing a aboveground to a hard-coded color. Unfortunately, pinch nary devices to debug specified hangs connected Windows, this forced america to amended the tooling to trim discrepancies pinch the proprietary driver arsenic overmuch arsenic possible. We upgraded the reverse-engineering inferior into a afloat WDDM2 logging layer, capable to analyse immoderate exertion tally pinch the charismatic Vulkan driver, and added support to dump the bid streams, registers, and shader code.
Compilers are not playing nice: Mesa is developed chiefly against GCC and Clang, and its codification guidelines relies connected a number of assumptions that don't guidelines erstwhile compiling pinch MSVC. For instance, MSVC doesn't work together pinch the different compilers connected really to grip enums: it tin construe a worth arsenic signed and limit them to 32-bit values, which tin consequence successful rather astonishing behaviors.
I've heard you liked opaque blobs: So let's put opaque information wrong your opaque API telephone - D3DKMTEscape. WDDM2 has the anticipation of vendor-specific hooks done that method, wherever the full contented is wholly up to the vendor. There is nary defined structure, and the only limitation is that it shouldn't replicate thing for an existing "standardized" call. Fortunately for us, it looks for illustration we tin safely disregard those for now, arsenic they look to attraction connected precocious features specified arsenic multi-GPU rendering.

...and beyond

The biggest unfastened mobility for making this production-ready is the interface to the proprietary kernel driver. Developing our ain KMD is not really an option, truthful we request to pass pinch AMD's. Right now, doing truthful relies connected reverse-engineered knowledge of backstage information structures — which is inherently fragile. To make matters worse, the UMD and KMD vessel together arsenic a matched brace pinch nary backward-compatibility guarantee, truthful these structures tin alteration betwixt driver versions pinch nary notice.

For RADV to go genuinely unchangeable and maintainable connected Windows, we request 1 of:

A stable, documented interface to the proprietary kernel-mode driver, or
A shim library that mediates connection pinch the KMD done the backstage information channel, giving america a unchangeable aboveground to build against moreover arsenic the underlying blobs evolve.

There is besides important activity remaining connected presentation. While Jesse Natalie did immoderate activity connected WSI for Windows (in the discourse of the Dozen driver), RADV presently only supports the slower CPU way for presentation. More activity is needed to alteration the usage of DXGI swapchains to amended performance, but that requires being capable to import an image from D3D12 (more opaque metadata, hurrah!). Even much activity would beryllium required to execute zero-copy swaps, the pinnacle of graphics pipelines. It could perchance lead to a 3x capacity summation for applications that are not GPU-bound. Accomplishing that past measurement would besides require nonstop engagement from AMD and astir apt Microsoft (as location are immoderate limitations related to image sharing). There are precedents to that benignant of quality-of-life improvements for open-source projects, truthful location is hope!

We dream this activity demonstrates the worth of an unfastened Vulkan driver connected Windows and helps motivate everybody progressive to activity toward that unchangeable foundation. For anyone interested, the activity is presently sitting successful this branch.

Thanks besides to Valve for sponsoring this shape of our activity to bring unfastened drivers to Windows.

User Interfaces of the Demo Scene

support@mycoding.id (MC Project) — Wed, 29 Jul 2026 13:42:08 +0700

User Interfaces of the Demo Scene

Exploring the devices of the trade

July 2026

Ahh, the demo scene - a integer creation subculture, a motley pack of imaginative nerds, and a favourite pastime of mine. So overmuch astonishing art, euphony and codification has been produced by sceners, and anyone who comes into interaction pinch the segment mightiness commencement to wonderment precisely how - isolated from hours of dedicated grind, of course.

The segment has a agelong and storied contented of building its ain tools. Sometimes from scratch, sometimes by stealing ideas aliases moreover codification from existing offerings. In a operation of teenage inexperience, aged habits, a penchant for experimenting and a desire to make things look cool, this has resulted successful immoderate alternatively peculiar personification interfaces.

A fewer of them are presented below. Most of them are for the Amiga, but respective different platforms are represented arsenic well.

Elite Sinus Producer

Not wanting to beryllium accused of clickbaiting, let's commencement disconnected pinch 1 of the main attractions: Elite Sinus Producer, made by Ipec Elite for the Amiga. Demos are usually described arsenic "real time", which is existent successful the consciousness that they're (almost never) conscionable animation players, and that demo effects are produced, framework by frame, from code. However, to execute seemingly intolerable method feats, extended "cheating" is employed. The astir communal cheat is astir apt the truthful called precalc, meaning that alternatively of doing analyzable maths connected a 7 MHz (or moreover slower) CPU, lookup tables are utilized. A plethora of devices for creating specified lookup tables exist. This is 1 of them.

When first starting Elite Sinus Producer, the personification is met pinch this menu. Upon pressing an F-key to make a selection, the corresponding paper action is highlighted and a sample of a cuckoo timepiece is played. Loudly.

Here I've selected the "Flower" option. Pretty, no? This tin past beryllium saved to disk arsenic (presumably) assembly root code. Any sprite would look bully erstwhile moving on this path!

There's besides a useful thief screen, which is truthful outlandish I had to drawback a short movie clip of it: Part of the inheritance consists of moving bluish raster bars, and the different portion flashes betwixt reddish and cyan. This evidently helps immensely erstwhile reference the text, displayed ina font designed pinch thing but legibility successful mind.

Text Based Interfaces

Plenty of segment related devices are either bid statement utilities aliases predominantly matter based. First out, we person the assemblers. There was a wide assortment of assemblers for the Amiga, but the segment ever favoured Seka, Asm-One and different derivatives of the aforesaid concept. There were truthful galore different versions and hacks (Trash'm-one, for example) that the sprawling family tree rivals that of Unix systems.

Here's Seka 2.0, and arsenic we tin see, it's based connected a commercialized assembler. This type of assembler ever asks the personification for the size of the moving representation to beryllium allocated. They past participate a bid statement mode, which tin beryllium utilized to analyse RAM representation and CPU registers, and load root files into the editor proper.

Here's AsmOne successful 1 of its galore incarnations. It's rather akin to Seka (in fact, it's "Seka-Updated"), but has much built-in commands and presumably different improvements arsenic good (I've ne'er been overmuch of a coder). Interface-wise, it opens its ain screen, arsenic opposed to moving successful a model connected the default Workbench screen.

What if you recovered a portion of cool euphony aliases graphics in, say, a game? What if you wanted to bargain immoderate samples, aliases a sprite, aliases possibly conscionable prevention an full tune for easy listening? Then you'd request a ripper - a instrumentality for hunting done your computer's memory, looking for remnants of specified information aft quitting the game. There were tons of various specified rippers for the Amiga. Here's Multi-Ripper, which has a reasonably typical group of features.

Here's different type of ripper, specifically written to look for Seka assembly sources successful representation aft the machine had crashed. The Amiga, for illustration astir different location computers, had nary representation protection, and demo coding is simply a notoriously crash-prone activity. Saving often was communal practice, but moreover seasoned coders sometimes messed up and forgot. With a spot of luck, the codification could beryllium extracted from representation aft a lukewarm reboot.

Here's different type of sine precalculator, called The Sinus Creator. I person nary thought if the numbers I've entered successful the screenshot make sense, but the two-window matter interface is interesting. Of course, the consequence tin beryllium saved arsenic a Seka root file.

Music Trackers

Demo euphony has historically been made successful trackers. Far from accepted notation, a locator lets the musician participate tones on pinch various modifiers and effects successful thing that's much reminiscent of a programming editor alternatively than regular composing. They besides fto the personification negociate instruments, whether sampled aliases synthesized.

Sample-based trackers connected the Amiga person an moreover much sprawling family character than that of Amiga assemblers, but they each originate from Karsten Obarski's commercialized Ultimate Soundtracker from 1987. Being commercialized and frankincense costing money, it was soon picked isolated by sceners, which resulted successful NoiseTracker, which was past revamped into ProTracker, which successful move exists successful truthful galore various versions and re-hashed hacks that it's nigh intolerable to support way (heh) of. The sprawl is even sprawlier than that of Amiga assemblers!

SoundMonitor 1.0 for the Commodore 64 (by Chris Huelsbeck) isn't strictly speaking a segment release, and wasn't called "tracker". However, the interface (with 1 "track" per disposable sound channel) is what inspired the antecedently mentioned Ultimate Soundtracker, and is frankincense included present for posterity and correctness.

NoiseTracker by the Swedish duo Mahoney and Kaktus wasn't the first, but it was immensely celebrated and came to specify the locator acquisition for years to come. Its bequest lives connected conscionable not successful Protracker connected the Amiga, but connected respective different platforms arsenic well.

Here's Protracker's record picker. Just for illustration successful NoiseTracker above, it's accessed by clicking the "Disk Op." fastener successful the alternatively dense interface. It's difficult to picture what a unusual acquisition this UI delivers, because it's almost - but not rather - intuitive to personification utilized to much mainstream Amiga programs. It's easy to misclick, misunderstand aliases conscionable plain miss things. To exemplify its idiosyncratic design, return statement of the vertical EXIT button, which quits backmost to the main menu. It's conveniently placed betwixt the up and down arrows utilized for scrolling the purple record and directory listing.

Here's Digicomposer 1.0 for the Atari ST/e, which successful move builds connected Noisetracker for the Atari. The interface is intelligibly much than conscionable inspired by its Amiga counterpart. It's but 1 of a full menagerie of trackers for the Atari ST, immoderate "digi" (sample-based) for illustration this one, immoderate for YM-chip based music.

Fasttracker II for MS-DOS is iconic successful its ain right. With support for Gravis UltraSound and different precocious PC sound hardware, it has features for 16-bit samples, bizarre amounts of sound channels, and moreover lets the personification play a elemental type of Snake if they truthful desire.

Abyss' Highest Experience is simply a chiptune locator for the Amiga, intended to sound for illustration the Commodore 64's SID chip. The personification interface is an absorbing crossover betwixt Soundtracker and a much modern, Workbench 2.0-like look.

I don't cognize overmuch astir Megatizer for the Atari ST, but it judge does look cool!

JamCrackerPro for the Amiga eschewed the accepted locator UI and opted for a system-friendly, multi-window interface.

Disk Copiers

In bid to administer demos (and pirated software), disk copying was a communal activity connected the scene. Commodore provided a disk copier successful AmigaOS, but it wasn't ever up to the task of copying demos and games that bypassed the record strategy by penning straight to the tracks of a floppy. Thus, typical package was needed!

Like SoundMonitor, X-Copy isn't strictly a segment release. Although primitively written by sceners, it was released commercially and past heavy pirated connected the scene. The prominently featured grids incorporate 1 quadrate for each way connected the disk, displaying the position for copying that peculiar portion of the floppy. When a transcript was vanished (which could return immoderate time), the programme helpfully played a small "boing" sound.

X-Copy was released successful galore versions during the Amiga heydays. Here's X-Copy 3.0, apparently successful an illicit assortment distributed by the cracking group Paradox.

Personally, I preferred D-Copy, mostly because I thought the personification interface looked cool (and I still do). It is, arsenic acold arsenic I know, a bona fide non-commercial segment product.

Other Tools

A short article for illustration this tin only ever scratch the aboveground of the galore demo segment devices ever created. Here are but a fewer much various devices and interfaces, selected for being absorbing and/or typical of their kind.

This is Titanics Cruncher for the Amiga. A cruncher uses asymmetrical compression of executable files. This saves abstraction connected disk, pinch the tradeoff being longer loading times, owed to the decrunching (decompression) of the executable upon moving it. Several different crunchers exist, and connected a assortment of platforms.

Before the Internet, location was the Bulletin Board System, aliases BBS. Sceners were usually willing successful Elite BBS:es, meaning ones that offered pirated package for download. There were few, if any, Elite BBS:es without cool ANSI (or PETSCII, connected the C64) graphics, for illustration successful animated paper screens. Thus, galore various ANSI editors popped up for different platforms. This is Digital Intelligence's Ansi-Editor v2.4 for the Amiga, and it has a very peculiar personification interface. The toolbar astatine the bottommost displays the presently progressive colours, but you can't prime them from there. Click each you want, to nary effect - you person to usage the pull-down paper to really prime one.

Before Twitter, Facebook, IRC and moreover wide entree to modems and BBS:es, scroll texts were the connection mean of prime for the scene. Cool scrollers required cool fonts, and it could beryllium adjuvant pinch a dedicated font (or charset) editor. Here's Charedit by Escape, for MS-DOS.

Home computers often came pinch civilization aliases different esoteric hardware. The Atari Falcon, for example, sported a Motorola 56001 integer awesome processor. DSPdit by tSCc (short for The Sirius Cybernetics Corporation) is an editor and assembler made specifically for DSP56k programming. It uses the modular GEM toolkit, which gives it a very cleanable and master look.

The first machine microorganism for the Amiga was a bootblock virus, infecting the footwear assemblage of floppy disks. As such, it could perchance ruin games and different floppies pinch civilization bootblocks. It was constructed by the Swiss Cracking Association, SCA. The microorganism dispersed for illustration wildfire and possibly SCA was plagued by a blameworthy conscience, because later they besides produced the very first microorganism slayer for the Amiga - designed to antagonistic their ain virus. The mega-mighty interface is easy capable to grasp!

As mentioned above, plentifulness of Amiga demos were truthful called trackmos, meaning they didn't usage the record system, but alternatively stored information consecutive connected the tracks of a floppy disk. This soon resulted successful various tooling becoming disposable for creating specified trackmos. Mostly it was conscionable codification shared betwixt sceners, but location were besides complete package suites, for illustration TrackmoDOS by Poison of NOVA. Among different things, it came pinch this orthodox record head for penning and deleting files to a trackmo floppy. Being a scener tool, the GUI of people sports immoderate alternatively spiffy colour gradients.

This is RAW, which isn't really a tool, but alternatively a disk magazine, aliases diskmag. A diskmag is conscionable that - a periodical published connected 1 aliases much floppy disks. Most of the articles were usually segment related, though immoderate mags branched retired and featured everything from short stories and poems to essays astir history and politics. RAW was 1 of the astir celebrated Amiga diskmags during the early 1990s, and arsenic we tin see, the UI was shiny and textured agelong earlier Frutiger Aero was a thing. It moreover came pinch a built-in palette editor (pictured), allowing the personification to customize the matter colours.

This is FuckPaint, a pixel creator for the Atari Falcon. It is included present solely connected the merit of its name, and its arsenic prestigious "Analizer" tool.

Although not astatine each a segment production, Deluxe Paint must beryllium mentioned here. To the champion of my knowledge, the segment ne'er produced a pixel creator for the Amiga (except later ports of Grafx2), presumably because cipher saw the request for one. I don't deliberation a azygous Amiga personification existed that didn't person a transcript of this software, either bought separately, bundled pinch the instrumentality aliases conscionable pirated. It was besides wholly ascendant connected the PC. There were different Amiga pixel painters, specified arsenic Brilliance and Personal Paint, but their mixed marketplace stock was a specified fraction of this giant, which besides dominated graphics creation for games good into the the mid-1990s.

That's capable demo segment interfaces for 1 helping. For those still wanting more, I urge this assemblage of utildisk menus.

Take attraction and happy hacking!

Transformer Transformer: A Unified Model for Motion-Conditioned Robot Co-Design

support@mycoding.id (MC Project) — Wed, 29 Jul 2026 13:42:08 +0700

A Unified Model for Motion-Conditioned Robot Co-design

Huy Ha, Karen Liu, Shuran Song

Not each robots are created equal

Not each robots are created adjacent — but what if you could creation 1 for a circumstantial task? We propose Transformer Transformer, a unified exemplary that does precisely this: manus it a manipulation demonstration, and it generates a complete robot — each link, joint, motor, and inertial spot — optimized for that motion. We fabricated 1 specified creation for cloth flinging connected an ALOHA² bimanual platform; it reduced search correction by 73% and max associated velocity by 30% versus the original.

Behind that consequence is simply a diffusion transformer trained connected RoboTokens, a unified tokenization of robot embodiments, states, and actions. The aforesaid architecture spans embodiment spaces (wheeled bimanual, quadrupeds, humanoids) and usage cases (embodiment generation, cross-embodiment control). Rather than overfitting to 1 reward function, it is simply a dynamics exemplary whose reward-agnostic predictions are converted into reward-specific worth predictions astatine conclusion time, past utilized to steer embodiment diffusion done a process we telephone Dynamics Self-Guidance. Experiments crossed 3 creation spaces show zero-shot optimization of unseen rewards and trajectories, improving capacity and runtime complete an evolutionary baseline.

Technical Summary Video

A 17-minute walkthrough — aliases scroll for the figure-and-caption version.

What is the champion robot
for a fixed manipulation task?

A robot's embodiment decides what tasks it tin do well. We move effort into making robots smarter — bigger datasets, amended algorithms, much tin argumentation architectures — but a bad embodiment tin hamper moreover a awesome policy. You tin cod each the tossing information successful the world, but if your robot's style is far from optimal, it mightiness flip itself alternatively of the ball.

So let's framework the problem concretely. Given arsenic input a target end-effector mobility and a reward function, we want to make a complete robot embodiment for the task — by complete we mean each link, joint, motor, and inertial property, positive a controller that drives the full thing. We telephone this motion-conditioned robot co-design.

A randomly procedurally-generated robot attempting the target motion: it falls earlier it can decorativeness 🙃

Demonstrate, Generate, Validate

Our model reframes robot co-design arsenic a three-step process: you demonstrate the desired end-effector mobility (e.g., from a UMI³ human demonstration), our exemplary generates an optimized embodiment, and the aforesaid model validates the creation by straight controlling it. The aforesaid web plays 3 roles — generator, critic, and cross-embodiment controller — by training jointly connected 1 unified tokenization of robots.

Three steps, 1 model. Given target end-effector motions (e.g., from a quality demo) and a user-defined reward, Transformer Transformer generates a complete robot — geometry, kinematics, inertia, motors — past controls that robot to way the motion. No abstracted optimizer, critic, or controller pipeline.

The insubstantial has 3 parts: a unified robot representation (RoboTokens), a unified architecture (Transformer Transformer), and a unified training objective (Dynamics Self-Guidance). You person until the architecture conception to fig retired the pun 🥸

PART 1

RoboTokens
A Unified Robot Representation

Every robot becomes a typed token sequence. Blue tokens encode embodiment (links, joints, motors); orangish tokens encode dynamics (states and actions). The aforesaid tokenizer ingests a quadruped, a humanoid, aliases a dexterous manus — nary per-robot adapter — truthful a azygous exemplary tin study crossed each of them.

If we want 1 exemplary to co-design any robot, the web needs a shared vocabulary that spans each embodiment. RoboTokens are that vocabulary: typed tokens for each link, joint, motor, state, and action — organized truthful a azygous series describes some a robot's embodiment (time-invariant) and its dynamics (time-varying).

RoboTokens has the pursuing cardinal creation goals:

Complete. A RoboToken series captures everything astir a rigid articulated robot: 5 embodiment token types (links, fixed joints, rotating/sliding joints, shot joints, motors) positive state and action tokens.
Flexible. Tokens reference each different by ID — a associated token knows the 2 nexus tokens it connects — truthful the aforesaid format handles a 6-DoF limb aliases a 35-DoF biped.
Consistent. The tokenizer canonicalizes redundant spatial offsets and stores inertial / toggle shape information successful a azygous frame, reducing the variance the exemplary has to study through.
Extensible. New token types slot successful without changing the architecture — we add target end-effector airs tokens for trajectory search by pointing them astatine the applicable end-effector and timestep IDs, nary retokenization of the remainder of the robot required.
Optimizable. Unlike autoregressive matter formats for illustration MJCF, diffusing continuous-valued RoboTokens gives some global controllability (later tokens power earlier ones across denoising steps) and differentiability — the 2 ingredients needed for gradient-based reward optimization astatine conclusion time.

We tokenized 11 robots from the MuJoCo Menagerie¹⁰, spanning 2 orders of magnitude successful wide (0.65 kg dexterous manus to 67.5 kg quadruped) and from 6 to 35 progressive joints. Each became a series of 28–101 RoboTokens — mini capable to diffuse end-to-end.

RoboTokens are 27–110× much compact than MJCF text. A robot's afloat explanation fits in tens of typed tokens (blue counts) wherever a connection tokenizer would request thousands (grey counts) — small capable that a transformer tin diffuse them directly, alternatively than autoregressively penning XML.

PART 2

Transformer Transformer
A Unified Architecture

The exemplary is simply a diffusion transformer (DiT¹) trained on RoboTokens pinch a DDIM⁴ sound schedule. Noise is added to each input token except the ones we information connected — and by changing which tokens are masked, the aforesaid web wears different hats. Mask everything to get an unconditional robot generator. Mask the actions and you get a cross-embodiment controller⁵,⁶. Mask the embodiment and you get a motion-conditioned robot designer. Same weights, jointly trained connected dynamics.

(And astir the pun: the first “Transformer” refers to robots whose embodiments can change. The 2nd refers to the self-attention architecture 🥸)

One architecture, 2 usage cases. (a) Motion-to-robot optimization jointly diffuses embodiment and dynamics tokens. (b) Cross-embodiment power diffuses the actions, conditioned connected the embodiment and the target motion. At conclusion time, the model's ain predictions are converted into a reward prediction whose gradients steer the procreation toward higher-reward designs.

An embodiment-aware controller

Because the controller sees the afloat embodiment arsenic input, it tin set its power strategy to the robot it's driving. Change nexus inertias, associated ranges, aliases centrifugal gains, and the aforesaid argumentation keeps search — without immoderate retraining per robot. We behavior-cloned whole-body controllers from RL experts⁷,⁸,⁹ trained one-per-design connected the procedural quadruped space.

One policy, galore quadrupeds. The aforesaid controller tracks the aforesaid trajectory across quadrupeds pinch continuous variations (link dimensions) and discrete variations (DoF, knee direction, spring-loaded vs serial leg).

A generator complete divers robot classes

Run the exemplary pinch nary conditioning astatine each and it diffuses retired complete robots from Gaussian noise. Trained connected divers robots from Menagerie¹⁰, a azygous Transformer Transformer produces fixed-base arms, quadrupeds, humanoids, and dexterous hands. Because diffusion is stochastic, each tally gives different robots.

Robots, diffused. A azygous exemplary generates fixed-base arms, quadrupeds, humanoids, and dexterous hands from the aforesaid sound schedule.

PART 3

Dynamics Self-Guidance
Zero-shotting Rewards

Now the existent co-design problem: really do we make a high-reward robot for a reward usability the exemplary has ne'er seen during training?

Here is the trick. The aforesaid exemplary predicts some the embodiment and its dynamics, truthful we tin feed those predictions into the user's reward usability and get a predicted reward. With predicted rewards successful hand, we tin tally the exemplary galore times successful parallel, foretell the reward for each candidate, and return the champion one. That is best-of-N sampling, but astatine GPU speed.

We tin do better. Because the predicted reward is simply a differentiable usability of the embodiment tokens, we tin return its gradient and inject it backmost into the diffusion process — pushing samples toward higher reward astatine each denoising step, pursuing classifier-guided DDIM¹¹. The exemplary is, successful effect, asking itself: “how should this robot change to summation the reward?” We telephone this Dynamics Self-Guidance.

Why “self-guidance”: related activity that incorporates dynamics into diffusion guidance trains a abstracted dynamics model¹² aliases relies connected a differentiable simulator¹³. Here, the exemplary uses its own dynamics predictions to steer itself.

One gradient, the full episode. Transformer Transformer predicts authorities and action tokens crossed the episode's sky successful parallel; the reward gradient past flows backmost done all of it to the embodiment tokens. Hardware gets optimized complete long-horizon dynamics, not 1 timestep astatine a time.

Reward in, robot out

Take a randomly-generated quadruped that falls during a move toss. Ask our exemplary to make a robot for the aforesaid trajectory pinch only a search reward and it lowers the halfway of wide and widens the support polygon — unchangeable creation choices it discovered arsenic a side-effect of trying to maximize tracking reward it has ne'er seen.

Now adhd a torque-penalty word and the optimization scenery shifts. The exemplary returns a smaller, lighter robot — still search the motion, but utilizing 30% little mean torque. Add a size punishment and the scenery shifts again. This is what group mightiness telephone zero-shotting rewards: train connected dynamics modeling, optimize astatine conclusion for immoderate reward you tin constitute down.

Same model, different conditioning, different robots. Swapping the target trajectory (Floor Scrub → Dynamic Toss) aliases adding a size punishment reshapes the optimization landscape, and the generated embodiments displacement accordingly — without retraining. Discrete choices (leg design, DoF) and continuous choices (link length, mounting point) some alteration successful response.

Composing diffusion models for full datasets

Manipulation isn't 1 motion; it's a distribution of motions. We want to optimize a robot for an full task — say, crockery washing — not a azygous trajectory. Diffusion composition¹⁴,¹⁵ lets america do precisely this: tally the exemplary successful parallel pinch different trajectories arsenic conditioning, mean the predicted sound at each diffusion step, and you get a generator optimized for each of them simultaneously.

We tested this connected the publicly-available UMI bimanual dish-washing dataset³, holding retired 26 validation trajectories. Transformer Transformer generated robots optimized complete the full held-out distribution — navigating some the discrete abstraction (spine designs: fixed, sliding, bending) and the continuous abstraction (arm dimensions, mounting offsets) — and the aforesaid model past served arsenic the cross-embodiment controller to validate each generated design.

One robot, a full task. Composed crossed the 26 held-out UMI dish-washing trajectories, the exemplary returns a azygous embodiment optimized for the full distribution — and the aforesaid web then drives it done each motion.

Better Designs, Faster

We comparison against 2 baselines: Random (sample a robot from the procedural design space) and CMA-ES¹⁶, a black-box optimizer wide utilized successful co-design. CMA-ES rolls retired campaigner robots successful MuJoCo¹⁷ pinch the creation space's controller (Mink¹⁸ for fixed-base / bimanual; an RL expert⁷ for legged robots), past biases the adjacent procreation toward higher-reward samples.

We springiness each methods the aforesaid task: make a robot for a target trajectory and reward, power it, study the achieved reward.

Test-time compute scales the results

Like ample connection models that get amended pinch longer thinking¹⁹, Transformer Transformer gets amended designs erstwhile fixed much inference seeds to sample over. Performance does yet plateau aft astir a infinitesimal — dissimilar the seemingly endless gains LLMs get from longer reasoning. We talk this successful the limitations.

Higher reward, orders of magnitude faster. Across 3 creation spaces and several reward functions, our Zeroth Order and Dynamics Self-Guidance variants some nutrient higher-reward designs arsenic much parallel samples are allowed — and some scope CMA-ES-level value successful seconds alternatively than hours. For multi-trajectory bimanual optimization (rightmost), CMA-ES took 3+ hours; our exemplary vanished in nether a minute.

Why truthful overmuch faster? Two reasons multiply. (1) GPU parallelization complete campaigner designs. (2) Non-autoregressive diffusion evaluates an full rollout successful parallel alternatively than 1 timestep astatine a time. Typical learned dynamics models foretell 1 measurement astatine a time; we foretell the full section astatine once.

Real-World: Cloth Flinging connected ALOHA

Original ALOHA vs Transformer Transformer-optimized ALOHA. Same task, aforesaid target trajectory, 2 robots. The optimized creation unfolds the cloth wherever the original cannot.

To stress-test the full strategy connected hardware, we picked move cloth unfolding connected ALOHA². Flinging²⁰ punishes embodiment mistakes done some kinematics (the limb has to scope complete the cloth astatine precocious speed) and unmodeled dynamics (aerodynamic drag, cloth weight, friction). The original ALOHA creation struggled to moreover way the trajectory.

We asked our exemplary to optimize the ALOHA creation for the “Tracking Velocity” reward, then fabricated the resulting design. Two things changed compared to the original:

Longer nexus lengths. Just capable to kinematically scope crossed the flinging trajectory, but ray capable to enactment wrong the Dynamixel centrifugal torque envelope.
Inverted mounting. The arms bent upside-down down the workspace, enabling an energy-efficient underarm plaything alternatively of an overhead fling.

Smoother associated trajectories. Joint velocity complete the flinging motion. The optimized design has less peaks and a little maximum than the original — the kinematic and move gains transportation to hardware.

Limitations & Future Work

Geometry and scene. RoboTokens presently screen primitive-based geometry, not arbitrary meshes aliases deformables, and don't yet encode the surrounding segment aliases interaction targets. Extending to analyzable geometry and segment discourse is simply a clear adjacent measurement toward afloat task-conditioned co-design.

Controller transfer. Our cross-embodiment controller correlates pinch the RL oracle at Pearson r = 0.53 connected diffused embodiments — promising but pinch headroom. Some outlier embodiments expose wherever the controller fails to generalize.

Test-time scaling plateau. Like different guided-diffusion methods, our test-time compute gains plateau aft astir a minute. Unlike LLMs¹⁹, more conclusion clip stops helping past a point. Improving reward-prediction accuracy and alternative conclusion schemes are unfastened directions.

Data procreation cost. RL-based whole-body controller information procreation is costly (one RL master per discrete creation choice, 16 hours of A100 per policy). Scaling Transformer Transformer to caller creation spaces hinges connected reducing this cost.

References

Peebles, W., & Xie, S. Scalable diffusion models pinch transformers. ICCV, 2023.
Zhao, T. Z., Kumar, V., Levine, S., & Finn, C. Learning fine-grained bimanual manipulation pinch low-cost hardware. arXiv:2304.13705, 2023.
Chi, C., Xu, Z., Pan, C., Cousineau, E., Burchfiel, B., Feng, S., Tedrake, R., & Song, S. Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots. Proceedings of Robotics: Science and Systems (RSS), 2024.
Song, J., Meng, C., & Ermon, S. Denoising Diffusion Implicit Models. ICLR, 2021.
Furuta, H., Iwasawa, Y., Matsuo, Y., & Gu, S. S. A strategy for morphology-task generalization via unified practice and behaviour distillation. arXiv:2211.14296, 2022.
Gupta, A., Fan, L., Ganguli, S., & Fei-Fei, L. MetaMorph: Learning cosmopolitan controllers pinch transformers. arXiv:2203.11931, 2022.
Ha, H., Gao, Y., Fu, Z., Tan, J., & Song, S. UMI connected Legs: Making Manipulation Policies Mobile pinch Manipulation-Centric Whole-body Controllers. Conference connected Robot Learning (CoRL), 2024.
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. Proximal argumentation optimization algorithms. arXiv:1707.06347, 2017.
Rudin, N., Hoeller, D., Reist, P., & Hutter, M. Learning to locomotion successful minutes utilizing massively parallel heavy reinforcement learning. CoRL, 2022.
Zakka, K., Tassa, Y., & MuJoCo Menagerie Contributors. MuJoCo Menagerie: A postulation of high-quality simulation models for MuJoCo. 2022. github.com/google-deepmind/mujoco_menagerie
Dhariwal, P., & Nichol, A. Diffusion models hit GANs connected image synthesis. NeurIPS, 2021.
Xu, X., Ha, H., & Song, S. Dynamics-Guided Diffusion Model for Sensor-less Robot Manipulator Design. arXiv:2402.15038, 2024.
Wang, T.-H. J., Zheng, J., Ma, P., Du, Y., Kim, B., Spielberg, A., Tenenbaum, J., Gan, C., & Rus, D. DiffuseBot: Breeding soft robots pinch physics-augmented generative diffusion models. NeurIPS, 2023.
Liu, N., Li, S., Du, Y., Torralba, A., & Tenenbaum, J. B. Compositional ocular procreation pinch composable diffusion models. ECCV, 2022.
Du, Y., Durkan, C., Strudel, R., Tenenbaum, J. B., Dieleman, S., Fergus, R., Sohl-Dickstein, J., Doucet, A., & Grathwohl, W. S. Reduce, reuse, recycle: Compositional procreation pinch energy-based diffusion models and MCMC. ICML, 2023.
Hansen, N., & Ostermeier, A. Completely derandomized self-adaptation successful improvement strategies. Evolutionary Computation 9(2): 159–195, 2001.
Todorov, E., Erez, T., & Tassa, Y. MuJoCo: A physics motor for model-based control. IROS, 2012.
Zakka, K. Mink: Python inverse kinematics based connected MuJoCo. 2025. github.com/kevinzakka/mink
Brown, B., Juravsky, J., Ehrlich, R., Clark, R., Le, Q. V., Ré, C., & Mirhoseini, A. Large connection monkeys: Scaling conclusion compute pinch repeated sampling. arXiv:2407.21787, 2024.
Ha, H., & Song, S. FlingBot: The unreasonable effectiveness of move manipulation for cloth unfolding. CoRL, 2021.

Full reference database disposable successful the paper. The action supra covers the citations utilized connected this page.

Hooray for the Sockets Interface

support@mycoding.id (MC Project) — Wed, 29 Jul 2026 13:42:08 +0700

Generated pinch AI.

In 1983, the University of California, Berkeley field (UCB) released type 4.2 of its UNIX operating system, the Berkeley Standard Distribution (BSD). For galore people, this is the ‘epoch event’ that defines the commencement of online services, because it is the type that released the BSD ‘sockets’ exemplary of networking into the world. In effect, the ‘right place, correct time’ mixed for computing systems and an operating strategy to tally connected them. This led to a monolithic summation successful entree to web services worldwide.

The installation of this first ubiquitous web attack was by tape, which you sewage successful the mail.

To modern eyes, this whitethorn look strange, fixed that modern operating strategy installation is astir often a network-driven event. If you do not download a afloat ISO image aliases bootable VMDK disk image from the network, you thin to download a smaller strategy that bootstraps from the network. This is normal — the web is assumed.

Back successful the 1980s, while ‘a network’ mightiness exist, it was some excessively constrictive — dial-up telephone astatine speeds arsenic debased arsenic 1200 aliases 2400 baud — and lacked communal protocols to specify really to find and fetch content. Even if you had entree to ‘the network’, it was difficult to reliably retrieve thing for illustration the binary codification of a caller operating system. It looked different almost everywhere.

Sockets are a ample portion of the communicative that changed this. After sockets, networked UNIX systems began to look broadly consistent. We arrived astatine a much standardized exemplary of web access.

Prior versions of BSD for PDP-11 minicomputers, making love backmost to 1978, had been released to the investigation organization worldwide connected tape: 30 copies of the codification for type 1, and 75 copies for type 2. The strategy was released nether a licence issued connected behalf of ‘the Regents of the University of California’, and it cautiously constricted liability and personification rights. You received the codification successful the station connected a reel of tape.

If you wanted to update your code, the Regents required continuity of the group signing the licences. This caused problems, arsenic world roles often shifted betwixt section members successful ways the Regents’ lawyers did not anticipate.

When you received an operating strategy for illustration this connected tape, it consisted of a linear bid of recordings. Each was denoted by an ‘End of Transmission’ (EOT) marker, which you could skip to scope the adjacent block. In effect, a watercourse of bytes was publication successful a fixed format.

To begin, you were provided pinch minimal hex codification to participate by manus astatine a move console. This depended connected your machine architecture. The codification bootstrapped by reference the first portion artifact into memory, aft which you jumped into representation to tally it arsenic a bootstrap loader.

Entering the hex meant mounting switches crossed a 16-bit section — up aliases down, connected aliases disconnected — checking them against a printed listing, and past committing them to representation 1 reside astatine a time. It was slow and error-prone. The existent end-of-tape marker was a metallic explanation that triggered a photoelectric diode to extremity the portion motor.

After loading the bootstrap loader, you could format a disk and unpack portion archive (TAR) information from the portion onto it. This encoded each operating strategy files onto the section disk. The process took astir of a day. If you needed to recompile the system, for example, to adhd instrumentality drivers, it could return 2 aliases 3 days. Despite this, galore group accepted the costs to summation entree to a UNIX system.

Version 4.2BSD shipped pinch respective important changes. These included bug fixes, an improved filesystem quota method, and symbolic links — which initially puzzled users accustomed to difficult links. Alongside these was the Berkeley sockets networking system, developed nether statement from the Defence Advanced Research Projects Agency (DARPA). At the time, it wasn’t clear really important this was.

Before sockets

Before this, entree to web services connected UNIX relied connected programs specified arsenic cu aliases tip. These acted arsenic terminal emulators, sending characters complete serial ports to modems. Many early modems were acoustic couplers attached to telephone handsets utilizing Velcro — 1 portion was a microphone, the different a speaker. You connected by dialling the phone.

Tools for illustration the Unix-to-Unix Copy Program (UUCP) utilized scripts that called cu aliases akin devices to found connections, past transferred data. Even utilizing cu was slow. Terminal types successful devices specified arsenic the vi editor (a BSD invention by Bill Joy, later a co-founder of Sun Microsystems) reduced surface updates to suit slow links.

UUCP was really we sent mail, publication web news, and received (small) files. For overmuch of the investigation community, it efficaciously was the network. Others utilized IBM BITNET aliases systems specified arsenic the UK Science and Engineering Research Council Network (SERC), later JANET (Joint Academic Network), which operated complete X.25 information networks.

Sockets and the UNIX record model

Sockets changed each this. They provided a simple, accordant measurement to link to ARPANET systems. Crucially, they reused an existing UNIX abstraction — the record descriptor. In elemental terms, a socket behaved for illustration a file.

By adopting the UNIX record descriptor model, almost immoderate programme that wrote to a record aliases publication a record could beryllium converted to publication and constitute to the network. Back-porting UUCP to activity complete ARPANET was not peculiarly hard, and convenient programs for illustration FTP for record transfer, Telnet for online access, and a back-end for the SMTP message strategy to talk to the UNIX Sendmail work (another characteristic of the BSD UNIX system) followed very rapidly. In a remarkably short abstraction of time, almost everything that could talk to sockets had been written to talk to sockets, aliases immoderate benignant of shim programme that talked to sockets for you.

Everything ‘just worked’. Or, successful the lawsuit of the first merchandise of 4.2BSD, it really didn’t work.

Part of the problem was that astir recipients of the 4.2BSD portion had nary entree to ARPANET. At the time, ARPANET was constricted to a mini group of US universities and investigation institutions, pinch only a fewer connections successful Europe and Asia via satellite. Most sites lacked immoderate web utilizing ARPANET protocols. For example, the assemblage I worked astatine had Ethernet, but astir machines connected to it via serial links alternatively than directly. We were fortunate to person 2 Virtual Address Extension (VAX) systems connected Ethernet, but 1 ran VAX/VMS and did not yet see the ARPANET networking code.

Sockets were ‘run’ by connecting them to UNIX-domain sockets, which resembled the existing tube mechanism. Pipes behaved for illustration typical files; they appeared successful the filesystem but contained nary disk data, and sounds depended connected writes from different process. To trial sockets, you could constitute C codification that connected to 1 of these section pipes. However, the strategy was still buggy — our first effort collapsed UNIX. As shipped, the sockets room couldn’t really pass pinch anything.

From research to standard

Sockets were not the only campaigner interface to the ARPANET protocols. AT&T Bell Labs was processing its ain exemplary for web I/O called ‘streams’, released arsenic STREAMS (the capitalization apparently mattered, tied to AT&T’s increasing inclination to patent aliases copyright thing pinch imaginable value). Like sockets, STREAMS presented an interface that intimately resembled record I/O, making it highly approachable for C programmers already comfortable pinch moving pinch files.

In practice, however, STREAMS was acold little accessible. Its emergence coincided pinch a ineligible conflict betwixt AT&T and BSD. As a result, the assemblage regents became much cautious astir distributing tapes, while AT&T itself grew progressively wary of releasing its codification to the wider world. Sockets, by contrast, had already arrived and were readily available.

Bill Joy’s caller company, Sun Microsystems, soon exploded onto the marketplace pinch a challenger to the VAX arsenic the default computing platform, this clip built astir the Motorola 68000 CPU. These machines ran UNIX, specifically, BSD 4.2 and its successors, and truthful came pinch the sockets strategy disposable retired of the box. Here was a desktop-class machine — a fast, high-resolution display, a afloat UNIX environment, Ethernet built in, and moving sockets codification from the infinitesimal it booted. What was not to love?

DEC responded successful this marketplace pinch the merchandise of ULTRIX, built connected progressively smaller VAX systems. ULTRIX drew heavy connected BSD UNIX, aliases astatine slightest incorporated important elements of it, and frankincense included sockets. One by one, awesome manufacturers abandoned their proprietary operating systems aliases introduced forms of UNIX compatibility, and successful doing truthful acquired sockets code.

Even AT&T, while processing what became System V arsenic a commercially ubiquitous type of UNIX, adopted sockets successful its networking code. The marketplace of ideas had already decided; contempt the appealing qualities of the streams model, networking meant sockets.

From that constituent on, advancement came from galore directions. Sun’s Network File System (NFS), built connected UDP sockets, became wide adopted. At the aforesaid time, shim layers emerged to thrust progressively affordable Ethernet cards for IBM PCs, exposing socket abstractions to Windows environments. In effect, web protocol improvement settled astir the TCP/IP and UDP/IP stack, accessed done sockets. Since then, large-scale invention has mostly taken the shape of refinement — improving performance, aliases adapting to IPv6 — until the presence of QUIC, which reimagines convention services complete UDP and sidelines TCP. And yet, moreover QUIC yet writes to a UNIX socket.

Sockets took complete the world.

The views expressed by the authors of this blog are their own and do not needfully bespeak the views of APNIC. Please statement a Code of Conduct applies to this blog.

DoorDash's CEO says AI coding is not enough to make engineers more productive

support@mycoding.id (MC Project) — Wed, 29 Jul 2026 13:41:49 +0700

Xu said that coding is only a mini portion of an engineer's day. Bloomberg/Getty Images

Vibe coding is conscionable 1 mini portion of the engineering puzzle, says DoorDash's CEO.

"Only definite parts of a package engineer's time are penning code, and it's awesome that we person models coming to constitute the codification for us," Tony Xu said connected an section of the "Uncapped" podcast released Tuesday. "That helps possibly the immoderate 25%, 30%, 50% of our clip that's really shipping code."

Xu cofounded the nutrient and grocery transportation giant successful 2013. DoorDash went nationalist successful 2020, 1 of the year's biggest tech IPOs.

The CEO said engineers walk different large chunk of clip successful merchandise reviews, creation meetings, and aligning pinch different business teams. Those aspects require AI too.

"If that doesn't alteration and travel together, you're not going to beryllium capable to conscionable person possibly the productivity summation that you dream to have," he said. "A bunch of companies crossed the manufacture are trying to fig retired and get those workflows correct truthful that you're not conscionable AI autochthonal from codification development, but you're AI autochthonal successful really you really operate."

For example, the institution still needs to activity connected parts of its business that tin beryllium improved pinch beingness AI, specified arsenic food-preparation robots and autonomous transportation vehicles, Xu said.

Xu has taken a mean attack to talking astir really AI has changed his company.

During a May net call, he said that AI has not yet changed DoorDash's organizational structure.

"We're seeing a batch of productivity gains correct now from AI, astir good northbound of half of our code, astir apt person to two-thirds of our codification is written by AI today," Xu said. "But that doesn't unsocial articulate really workflows and squad setup ought to change."

He joins a fistful of tech leaders who person spoken publically astir the limitations of AI and its usefulness to a company.

Earlier this month, Avishai Abrahami, the CEO of Wix, a web creation institution that acquired the vibe-coding level Base44, said the tech manufacture is overselling AI.

"I would emotion to opportunity the squad successful 3 years, we will beryllium a 1000 group successful Base44 and a 1000 group successful Wix, and everything other would beryllium AI agents. I'm not truthful judge it is possible," Abrahami said. "I deliberation that we each springiness excessively overmuch in installments for AI and what it tin do."

Even the man who coined the word vibe coding has been nationalist astir its galore limitations for engineers.

In an April talk, erstwhile Tesla AI caput and OpenAI founding member Andrej Karpathy said that codification written by AI is still messy.

"Sometimes I get a small spot of a bosom onslaught because it's not for illustration ace astonishing codification needfully each the time," he said. "It's very bloaty, and there's a batch of copy-paste, and there's awkward abstractions that are brittle, and it works, but it's conscionable really gross."

The 3 biggest takeaways from Mark Zuckerberg's media blitz

support@mycoding.id (MC Project) — Wed, 29 Jul 2026 13:41:49 +0700

Mark Zuckerberg went connected a media blitz pinch an unfastened letter, an op-ed, and a NYT interview. Tom Williams/CQ-Roll Call, Inc via Getty Images

Mark Zuckerberg is making his biggest nationalist push yet to style the AI debate.

Within a week, the Meta CEO published an unfastened letter, penned a Wall Street Journal op-ed, and sat down pinch The New York Times for a uncommon interview.

Across each 3 appearances, Zuckerberg laid retired a accordant message: AI should beryllium open, wide distributed, and built to empower individuals — not switch them.

Here are the 3 biggest takeaways from his media blitz.

AI shouldn't beryllium centralized

Zuckerberg many times said that concentrating advanced AI successful the hands of a fewer companies would beryllium some economically and politically dangerous.

In Zuckerberg's unfastened letter, he said that America's "advantage is decentralized and unfastened innovation," and that closing AI models to forestall China from gaining access to them will "only disadvantage the US and its allies."

Zuckerberg echoed that position successful his New York Times interview.

"So overmuch of the sermon from a batch of the different labs that are processing this is overwhelmingly filled pinch doom," Zuckerberg said. "There needs to beryllium a sound aliases respective voices that are bringing realism to this debate."

"I deliberation it is virtually intolerable to person a azygous benevolent superintelligence that is simultaneously aligned pinch everyone astatine once," Zuckerberg added.

Zuckerberg doesn't deliberation AI will termination jobs

Zuckerberg is not buying the AI occupation market pessimism

"I don't understand why anyone who believes that AI will destruct astir jobs and overmuch of humanity's relevance would unreserved to build that future," said Zuckerberg successful his effort successful the Journal.

Instead, he said that broadly disposable AI will create "more jobs successful the future, not fewer," making it easier to commencement businesses and fueling a much entrepreneurial economy.

Invention, not automation, is AI's biggest opportunity

Rather than framing AI chiefly arsenic a instrumentality to automate work, Zuckerberg based on its top effect will travel from helping group invent wholly caller things.

"Invention, not automation, will beryllium the top publication of superintelligence," Zuckerberg wrote successful the Journal, adding that AI could accelerate discoveries ranging from caller medicines to breakthroughs that thief group build businesses and lick problems.

"The history of populist and economics has shown that location is nary azygous nonsubjective reply to really group specify the champion life," Zuckerberg added, "And truthful the champion attack is letting group determine what matters to them."

Zendaya says Tom Holland has a quality she wishes she had more of

support@mycoding.id (MC Project) — Wed, 29 Jul 2026 13:41:49 +0700

Zendaya says Tom Holland is awesome astatine mounting boundaries. Christopher Polk/Deadline via Getty Images

Zendaya said Tom Holland is amended than she is at mounting boundaries.

Speaking alongside Holland successful a associated question and reply pinch French YouTuber HugoDécrypte connected Monday, Zendaya said she admired Holland's expertise to tie clear lines and to guidelines up for himself and others.

"Something I americium ever impressed pinch Tom about, which I consciousness for illustration I want to inherit much of," Zendaya said. "You're a large stickler for respect and kindness."

She said Holland makes his expectations astir respect and kindness clear from the outset, past firmly enforces those boundaries if they're crossed.

On the different hand, she tends to beryllium much conciliatory and much apt to soft things complete than tie a difficult line, she said.

"You're bully astatine mounting the boundaries really clearly, really strong," Zendaya said.

"I for illustration that you really cognize what you consciousness is correct for yourself, and you do it, and for others. You're very protective successful that way," Zendaya said.

True enough, erstwhile it was his move to answer, Holland ended the speech pinch a joke.

"Speaking of boundaries, I'm not going to stock my astir valuable portion of proposal I sewage from Zendaya," he said.

The mates — and longtime costars — person antecedently spoken astir the power they've had connected each other.

In a 2024 question and reply pinch Vanity Fair, Zendaya said it's "strangely comfortable" acting alongside Holland, pinch whom she's been successful a nationalist narration since 2021.

"It's for illustration 2nd nature, if anything. You consciousness other safe pinch the personification you're acting beside. I emotion moving pinch him. He's truthful talented, and truthful passionate astir what he does. He ever gives things 1 1000 percent, moreover if he's perfectly worn down," she said.

Earlier this month, Holland said he initially hesitated to judge a role successful Christopher Nolan's "The Odyssey" because filming would overlap pinch "Spider-Man: Brand New Day." Zendaya, he said, jokingly told him she'd "leave" him if he turned it down.

The scheduling conflict was yet resolved aft Sony delayed production, allowing the mates to look successful some films.

"The Odyssey" deed theaters connected July 17, while "Spider-Man: Brand New Day" opens July 31.

MC Project

AI Recognizes 96% Of Brands But Mentions Almost None, New Study Finds

In This Study Summary

The Study Methodology: Measuring AI Recognition vs. AI Mentions

Measuring AI Recognition

Measuring AI Mentions

Expanding The Analysis

AI Recognizes Far More Brands Than It Mentions

So What Separates The Brands AI Mentions?

AI Trusts Different Sources At Each Stage Of The Buyer Journey

What Else The Data Showed

Key Takeaway

What are autonomous AI agents?

How do autonomous AI agents work?

1. Receiving a goal

2. Creating a plan

3. Breaking the scheme into smaller tasks

4. Gathering accusation and utilizing tools

5. Evaluating the results

6. Adapting to caller information

Core components of an autonomous AI agent

Levels of autonomy successful AI agents

Assisted

Semi-autonomous

Autonomous

Autonomous AI agents vs different AI systems

Where are autonomous AI agents used?

What are the benefits of utilizing autonomous AI agents?

What are the risks of utilizing autonomous AI agents?

When should you usage autonomous AI agents?

Should you build autonomous AI agents?

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User Interfaces of the Demo Scene

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Other Tools

Transformer Transformer: A Unified Model for Motion-Conditioned Robot Co-Design

A Unified Model for Motion-Conditioned Robot Co-design

Not each robots are created equal

Technical Summary Video

What is the champion robot for a fixed manipulation task?

Demonstrate, Generate, Validate

RoboTokens A Unified Robot Representation

Transformer Transformer A Unified Architecture

An embodiment-aware controller

A generator complete divers robot classes

Dynamics Self-Guidance Zero-shotting Rewards

Reward in, robot out

Composing diffusion models for full datasets

Better Designs, Faster

Test-time compute scales the results

Real-World: Cloth Flinging connected ALOHA

Limitations & Future Work

References

Hooray for the Sockets Interface

Before sockets

Sockets and the UNIX record model

From research to standard

DoorDash's CEO says AI coding is not enough to make engineers more productive

The 3 biggest takeaways from Mark Zuckerberg's media blitz

AI shouldn't beryllium centralized

Zuckerberg doesn't deliberation AI will termination jobs

Invention, not automation, is AI's biggest opportunity

Read next

Zendaya says Tom Holland has a quality she wishes she had more of

Read next

What is the champion robot
for a fixed manipulation task?

RoboTokens
A Unified Robot Representation

Transformer Transformer
A Unified Architecture

Dynamics Self-Guidance
Zero-shotting Rewards