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Last week, MIT CSAIL published Sparks of Weight-Space Programming. Given the raw weights of a small network, GPT-6 Astra could inform which equivalent it had been trained to play. Given lone a game’s rules, it could compose the weights of a small network from scratch and attack networks trained alongside PPO for 100 myriad steps. No gradients, no training loop, fair reasoning concerning what the weights compute. They coined this weight-space programming.

In another experiment, they asked Astra to create Qwen3.5-0.8B improved at math by editing its weights. Astra changed lone 0.016% of the weights, and MATH-500 accuracy jumped from 7.9% to 48.1%.¹

Looking at Astra’s edit, I realized most of its hunt was solving a linear program by test and error. It spent concerning 8 hours and 28 onward passes choosing which weights to alter and by how much. A brace of LP solves, concerning a infinitesimal each, do the identical job alongside 8× small changed weights. In this article I’ll stroll through the edit, why it’s an LP, and what that says concerning the fix itself.

Thinking loops#

Small reasoning models have a average failure: they get stuck in thinking loops, repeating the identical few lines complete and complete alternatively of finishing their reasoning. Qwen3.5-0.8B is particularly prone to it, and its example cardstock says so. On MATH-500, 93% of the basis model’s answers ran into the 32,768-token bounds without always reaching an answer.

The edit fixes the loops, so it helps anyplace the example loops. That includes education following: IFEval went from 21% to 62%.

On Gemma 3 270M, which rarely loops, Astra couldn’t meaningfully enhance its math ability. As the authors put it, editing the weights could eliminate an obstacle hiding an ability, but couldn’t add one the example lacked.

How Astra fixed the loops#

Transformers use residual connections: all layer’s output is added to its input fairly than replacing it. Follow one token through the network and you get a sole vector that all tier adds to, called the residual stream (1,024-dimensional in this model). When the example is looping, the residual stream shifts in a accordant direction. Astra’s fix was to edit two layers so that what they add points the contrary way.

Finding the iteration direction#

Astra wrote four pairs of abbreviated texts, all a part of productive reasoning and a looping type of the identical reasoning. It ran all eight through the example and recorded all token’s residual stream vector at one layer. Averaging complete the looping texts gives one vector, and averaging complete the productive texts gives another. Their difference, normalized, is the loop direction $u$: the way the residual stream moves whenever the example loops.

Editing the weights#

Each MLP ends alongside a matrix $W$ (called down_proj). At all token, it turns the MLP’s 3,584-dimensional hidden activation $h$ into a 1,024-dimensional output $Wh$, which gets added to the residual stream $x$:

$$x \leftarrow x + Wh, \qquad Wh = \sum_j h_j \, w_j$$

So the MLP’s output is continually a valued sum of the identical 3,584 vectors, the columns $w_j$ of $W$. $W$ fixes what those vectors are; the activations $h_j$ decide how much of all one gets added at all token.

Each pillar points partially alongside $u$. That amount, $u^\top w_j$, is how much pillar $j$ pushes the example toward looping whenever $h_j$ is active. Astra’s edit subtracts a multiple $\alpha$ of it:

$$w_j \to w_j - \alpha \, (u^\top w_j) \, u$$

At $\alpha = 1$ the component alongside $u$ is gone, so the pillar no longer pushes toward looping. Doing this to all pillar in layers 15 and 19 took accuracy from 9% to 33% on GSM8K and MATH training problems. At $\alpha = 6$ the component is flipped: a pillar that wrote $+a$ alongside $u$ now writes $-5a$, and pushes the example away from looping whenever it’s used.

Every edited pillar changes by a multiple of the identical vector $u$. So the entire edit is one direction $u$ affirmative one figure per column, $v_j = -\alpha\,(u^\top w_j)$, which is zero for columns remaining alone. That’s a rank-1 change:

$$\Delta W = u\,v^\top$$

At all token, it adds $(v \cdot h_t)\,u$ to the residual stream: several amount of $u$, set by how energetic the edited columns are. No another direction changes.

That leaves $v$ to choose: which columns to edit, and how ample $\alpha$ should be. Astra worked it out by test and error, evaluation all edit on a caller lot of those training problems:

Pushing harder than $\alpha = 1$ helped more, and a random direction did nothing. The final edit used $\alpha = 6$ on lone the 64 columns per tier that wrote the most $u$ on looping text.

Where the period went#

The document released Astra’s complete logs: all tool call, all onward pass, all note it wrote to itself. Astra had a Python kernel alongside the weights loaded and a prosperity of 50 onward passes, all a batched run of the example on training problems or texts of its choosing.

The edit was icy following concerning 10 hours, and the final 17 passes re-tested it without changing anything.² Of those 10 hours, finding $u$ took 15 minutes. Finding $v$ took concerning 8 hours.

Choosing v is a linear program#

Astra was searching for a $v$ that powerfully pushes looping tokens distant from $u$ during barely touching productive ones. Since the shove at token $t$ is $v \cdot h_t$, linear in $v$, the two requirements are linear inequalities.

I tagged tokens from the basis model’s own traces: looping tokens ($R$), profound inner a loop, and productive tokens ($P$), fine before any iteration starts,³ and recorded their activations $h_t$ at layers 15 and 19. Each one becomes a constraint, and the LP picks the $v$ alongside the smallest L1 norm $\sum_j |v_j|$:

$$ \begin{aligned} \min_v \quad & \sum_j |v_j| \\ \text{s.t.} \quad & v \cdot h_t \le -\delta && t \in R \quad \text{(at smallest } \delta \text{ distant from } u\text{)} \\ & |v \cdot h_t| \le \varepsilon && t \in P \quad \text{(at most } \varepsilon\text{)} \end{aligned} $$

In custom no $v$ satisfies all constraint, so all one gets a penalized slack variable.

Minimizing L1 tends to create $v$ sparse. The L1 domain is a diamond, and whenever you develop it until it touches the feasible region, it normally touches at a corner, anywhere most coordinates are exactly zero. So the LP picks the columns as fine as the amounts.

To get exactly 8 columns per layer, I kept the 8 alongside the largest consequence and solved again alongside the remainder fixed at zero.⁴

That replaces Astra’s 8-hour search, including its choice of columns and $\alpha$, alongside a brace of LP solves. An off-the-shelf solver (HiGHS) took complete 20 minutes all on these constraints, so I wrote a GPU solver (PDHG, the algorithm rearward PDLP) that gets inside 0.2% of its optimum in concerning a minute.⁵

Results#

I tested two LP edits. Both use Astra’s layers. One uses Astra’s direction $u$. The another uses a heuristic direction: tag tokens in the model’s own traces as looping or productive alongside a uncomplicated repeat detector, afterward obtain the difference of their average residual streams. It’s the identical formula Astra used, minus the hand-written texts, so no LLM is involved. I tuned the LP’s settings on a distinct 128-problem dev set; everything in the diagram and array is on data never used for fitting or tuning.

With Astra’s direction, the LP matches Astra’s edit on all benchmark, inside noise, during changing 8× small weights. With the heuristic direction, it gets concerning 93% of the way there.

A few another things showed up:

• The hunt mattered, and the LP replaces it. On the dev set, reusing Astra’s formula (top 64 columns, $\alpha = 6$) on the heuristic direction without searching again scores 44.5. The LP scores 54.7.
• Different columns, identical fix. The LP’s 8 columns portion lone 3 (layer 15) and 1 (layer 19) alongside Astra’s 64. Many sparse solutions work; what matters is the total shove alongside $u.$
• A random direction does nothing. On the dev set, a random $u$ of the identical size scores 10.2, against 10.9 for the basis model.

A steering vector does the identical job#

An edit can lone inform a looping token distinct from a productive one through its activations, and a iteration isn’t recognizable from them until it’s fine under way.⁶ So on the dev set, asking the LP to shove lone on loop-like tokens did no improved than pushing on all token.

The LP’s 8 columns in tier 15 are energetic on 89% of tokens, and the shove they add barely varies from token to token, so the edit is near to a changeless shove alongside $u$. A changeless shove doesn’t need a importance edit. It can be added immediately as a steering vector, a fixed vector added to the residual stream at all token.

The array below adds the LP edit’s average shove alongside $u$ to the unedited example at layers 15 and 19, at three strengths:

For comparison, on the identical runs: the LP edit scores 62.5 on the dev set and 53.2 on MATH-500, Astra’s edit 59.4 and 50.0, the basis example 10.9 and 7.0. MATH-500 current is one example per problem.

At 1×, the steering vector recovers basically the entire fix. The opening is narrow: at 0.5× the loops arrive back, and at 2× they halt but the reasoning falls apart.

The lone difference is delivery. The LP edit is an average checkpoint; the steering vector needs additional code at conclusion time.

Limitations and what’s next#

This is one model. The LP motionless relies on Astra for the layers, and the finest results use Astra’s direction too. The heuristic direction gets most of the way without an LLM, but not all of it.

Loops additionally turned out to be fixable alongside a changeless push, so the LP’s per-token constraints didn’t end up mattering much. A behavior that should alter several tokens and depart others solitary would be a improved test of them. Choosing the layers without an delegate is the another open piece.

Lastly, this was lone tested on removing loops. Many another behaviors, akin refusal or sycophancy, can be steered by adding or removing a sole direction in the residual stream.⁷ If a behavior can be steered alongside one direction, choosing how to build it into the weights have to be the identical LP, but that motionless needs testing.

Code, edits, and the complete write-up are on GitHub.

Citation#

Please citation this activity as:

Barry, Nathan. "Can a Linear Program Do Weight-Space Programming?" nathan.rs (Nov 2026). https://nathan.rs/posts/weight-space-lp.

Or use the BibTex citation:

@article{barry2026wslp, heading = "Can a Linear Program Do Weight-Space Programming?", author = "Barry, Nathan", diary = "nathan.rs", twelvemonth = "2026", duration = "Nov", url = "https://nathan.rs/posts/weight-space-lp" }

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Serve Robotics is beginning up its phase to create its autonomous last-mile shipment robots accessible to anyone who wants to use them. The business is launching a new AV Delivery Developer Portal, enabling third-party application engineers, autonomous businesses, and equal autonomous AI agents to admission its fleet of 2,000 sidewalk robots.

With this new capability, third-party developers and curious small businesses can use Serve’s application tools to dispatch robots immediately without relying on important food shipment companies, akin Uber Eats. Now, anyone who lives in among the 150 neighborhoods anywhere the business currently operates can employ a sidewalk robot for deliveries.

Serve, which was spun out of the app-based shipment business Postmates in 2021, has been making Uber Eats deliveries in Los Angeles, Miami, Dallas, and dozens of another cities for multiple years now, using robots that appearance akin autonomous buying carts. Serve says its shipment robots can be a safer substitute to cars and assistance decrease traffic congestion since they run chiefly on sidewalks. Since the robots have a top speed of about 6 mph, there’s small to no hazard to pedestrians.

In an discussion alongside The Verge, Serve CEO Ali Kashani explained that beginning the phase keeps the business from relying too much on big shipment apps, during additionally keeping the robots occupied between the lunch and meal rushes.

“The actuality of food shipment platforms is that normally they dominate their markets,” Kashani said. “Food is mostly extremely picky. So you have lunch, you have dinner. But afterward there are times in between. So again, having that diversification helps.”

By turning the fleet into what Kashani describes as an open marketplace, Serve’s robots volition treat API requests the identical as endeavor orders. “There’s no preferential care at the end of the day,” he said. “Until the robot is on a delivery, it doesn’t have a dedication to anybody.”

For small merchants, Kashani says the pay-per-delivery or hourly booking options volition undercut human-powered shipment services: “This would be a fraction of that cost.” Serve’s throw additionally extends to intra-office shuttles, repair shops ferrying parts, and peer-to-peer exchanges akin Facebook Marketplace. Developers provision the pickup and dropoff coordinates during Serve’s rear end orchestrates obstacle avoidance, routing, and distant teleoperation supervision. Instead of hiring couriers or sending younger personnel throughout town, businesses could program Serve’s robots to shuttle items between offices.

For Kashani, beginning fleet admission is the next phase in a long-standing crusade against last-mile shipment economics. “Why does getting item delivered from China disbursal $1, but from Chinatown disbursal $10?” Kashani said.

The issue is that shipment apps akin DoorDash and Uber Eats obtain a enormous 15 percent to 30 percent cut on all order, mostly since paying individual drivers is expensive. By letting businesses nexus immediately to its robot fleet through software, Serve allows local shops — akin bakeries, florists, and pharmacies — to add robot shipment direct to their own websites and checkout registers without paying those steep middleman fees.

It’s improved economics for Serve, too. Kashani connects lesser per-delivery expenses immediately to fleet use and scale, arguing that expanding petition beyond highest eatery hours into off-peak hours and new use cases drives general functioning and hardware expenses downward for everyone.

Yet sidewalk autonomy remains governed by the laws of physics. Serve says admission to its robots volition motionless be strictly gated by manual endorsement by Serve’s workforce to forestall hazardous deployments or illicit cargo. Liability on taken or damaged products volition additionally be topic to how idiosyncratic partnerships are priced and insured, Kashani noted.

And don’t anticipate 50-pound sidewalk robots to morph into full-size vehicles. Kashani remains adamant that Serve won’t morph into a street-based shipment assistance anytime soon. If application developers desire to automate the sidewalk, Serve volition gladly market them the wheels.

In my recent blog post I remarked that repository benchmarking is in a crisis because AI agents commencement to overfit on idiosyncratic benchmarks with questionable techniques, which drives implementation times towards zero. This has happened alongside TPC-D in the past, and broke the benchmark. TPC-H tries to fight that alongside rules, but most group who publish (unofficial) TPC-H results simply disregard the rules. In blend alongside overly enthusiastic AI agents, this leads to ruthless overfitting.

We are seeing the next flat of that alongside the GenDB-V system. The corresponding document commands “Stop Handcrafting Your Engine! GenDB-V: Towards AI-Generated and Formally Verified Database Systems”, and reports speedups of 22× to 61× complete Umbra and DuckDB, and all that in a completely verified system. These are certainly impressive claims, and would propose that traditional repository engines are in trouble.

Taking a stage back, though, it have to be apparent that a performance improvement of a aspect of 22 complete Umbra is infeasible, at smallest if we are talking concerning traditional query processing. At SF10, the 22 TPC-H queries peruse approximately 15GB of (compressed) basis data, and on a 9950X3D Umbra runs all queries in a total of approximately 540ms. On that device the memory touched for data divided by the recollection bandwidth (54GB/s) alone is concerning 280ms; item below that is infeasible. Better compression might bring that downward a bit, but not by an command of magnitude.

Unsurprisingly, the most theatrical gains of GenDB-V arrive from precomputation. The agents have carefully handcrafted auxiliary data structures for all query, which are computed at burden time, and which avoid most of the work. The bodily scheme is heavily tuned to the queries, too, for example, there is an additional construction that lists the supplier rows in s_acctbal DESC order, which makes Q2 trivial. Similar per-query data structures are built for most queries. And we see that in the achievement numbers: Several queries already run in near-constant period at SF100: Q2 in 0.7 ms, Q17 in concerning 15 ms and Q4 in concerning 17 ms on one thread. That is the TPC-D symptom, without stored aggregates.

There is additionally a “plain-array” mode, which, at archetypal glance, is more fair, as it avoids these excessive preparations and additionally reports performance improvements of fair approximately a aspect of 3, which seems achievable. But whenever looking closely, we see that current the agents did something equal additional insane: They hard-coded their cognition concerning the data generator. They “know” that all customer k volition be at row k-1. And they cognize that dbgen numbers the i-th command 32·(i/8) + i%8, so key k have to be at row 8·(k/32) + k%32 − 1. Which makes the associate in query Q18 excellent cheap.

How does all this fit together alongside the assertion of a completely verified database engine? Well, the code does inspect if the hard-coded knowledge concerning the data generator holds in practice, e.g., by checking if the guessed associate partner row is correct. If not, it falls rear to a super dilatory generic nested iteration implementation, but as that never happens with the authoritative TPC-H data generator, that code way is irrelevant in the reported experiments.

Which brings me rear to my first blog article claim: Letting AI agents optimize for a stated repository benchmark makes the benchmark useless. They volition do their finest to discover insane tricks to speed up this particular benchmark, but none of that would activity in a genuine system. The high tuning and synthesis period aside, deleting a sole tuple (or inserting one in the middle) can most apt logic the entire method to fall off a cliff, as it forces the dilatory fallback path. Which is not something you desire in a manufacturing system.

5 min read

By analyzing NASA lidar data, my AI agents discovered a Maya location in Mexico that, as far as anyone can tell, no one had reported before. It took less than 72 hours to go from a random idea during traveling to an authoritative answer from the Mexican government. This is by far the finest period always to be alive.

Disclaimer: I'm not an archaeologist.

I'm going to inform this narrative as a diary, since the timeline is likely the finest part.

18 October

I was traveling in Peru, Gemini-ing concerning the former of the place, whenever I learned concerning Hiram Bingham, the American explorer who "discovered" Machu Picchu in 1911. "What luck!" I thought. He was really looking for a distinct city, Vilcabamba, the final refuge of the Incas, whenever a local farmer in the vale below guided him to the ruins. "Imagine if Bingham had our tools," I thought. "What would he do?"

I brainstormed a bit alongside a few agents (Gemini, ChatGPT, Claude and Grok), and they all stated lidar. From above, a ruin in the jungle is fair a bunch of trees (like Machu Picchu before it was cleared), so moon photos don't assistance much. Lidar, instead, fires laser pulses through the gaps in the canopy and maps the dirt underneath.

So the agents went looking for lidar data. Peru isn't fine covered, and hiring a business to do a study meant permits, expenses and bureaucracy, so I gave up on that. Then I thought: why not the Maya? Their area is much improved covered by community lidar.

That night, I asked the agents to analyze it and went to bed.

19 October

I woke up and checked the agents' reports. 'DAMN!' They had established many of villages and another regular shapes that looked akin structures or houses.

So how did they do it? They turned the lidar strips (about 1,200 km² of jungle) into images, and 48 agents went through them one by one akin extremely tolerant archaeologists, looking for pyramids, platforms, and plazas during filtering out natural hills, sinkholes, and roads. There was no particular example or classifier involved: the agents exactly looked at the pictures, following a exact guide.

Me: That's awesome! Please inspect which of these villages haven't been discovered yet. Search the two the writings and the archives.

…a few hours later…

Agent: One important settlement doesn't appear to have been registered.

This is the hilltop from the top of this post. It sits on the border of a cliff, under the jungle, and it has direct edges and keen corners. Nature doesn't really do direct lines.

Zooming in, there's a levelled plaza, an oval pyramid at smallest 8–12 m tall, a raised courtyard attached to it and a flat-bottomed hollow that power have been a h2o reservoir.

And this is what it looks akin in 3D.

Me: OK, what should I do?

Agent: You should communication INAH (Mexico's National Institute of Anthropology and History), compose a study on your discovery, and inspect whether the location is in their register. Sometimes they have unpublished results.

Me: OK, how do I communication INAH? How does the reporting procedure work?

Agent: …instructions for the report… + email addresses

Me: OK, delight do the report.

And I went to bed.

20 October

I iterated on the study a bit, since a few things were off, and translated it into Spanish to enhance the chances of getting a answer (at least, that's what Claude suggested). I don't conversation Spanish, so I had to rely the agents on that one.

The agents additionally checked the raw item cloud, to create certain the shapes were really on the dirt and not created by the trees. Green dots are leaves, brown dots are the ground.

Then I sent the study to INAH.

21 October

INAH replied: they checked their records and established no registered archaeological location at that location.

To be clear, it's not authoritative yet. But as far as anyone can tell, nobody had put this one on a map before.

So what was it?

This part is speculation, from a lengthy conversation alongside the agents. It looks akin a small Maya civic centre from the Classic duration (roughly AD 250–900), basically the "downtown" of a local elite. The plaza is anywhere group gathered, for ceremonies and perchance equal a market. The pyramid was likely a temple, and the raised courtyard next to it power have been anywhere the local ruler lived. The hollow could have been an aguada, a rainwater reservoir: Maya towns needed them to get through the dry season. Or it's fair a hole. The three platforms about were likely houses.

I asked Gemini to ideate how it looked rear then:

For akin studies, have a appearance at how NASA's G-LiHT device plant (Cook et al. 2013) and how lidar collected for ecological investigation can be reused for archaeology (Golden et al. 2016). In 2024, archaeologists established a entire Maya city in Mexico this way (Auld-Thomas et al. 2024), others used lidar to evaluation how many group lived in the Maya lowlands (Estrada-Belli et al. 2025), and another study established what may have been ancient Maya marketplaces (Šprajc 2026).

What's next

In the study I offered to assistance alongside a site inspect led by INAH. In Mexico, archaeological sites are protected by national law, and any fieldwork needs INAH's authorisation, so it's now up to the local INAH centre to decide whether to dispatch a team. If they go, I'll article an update.

What I've learned

You can fair do things. I'm not an archaeologist, so I didn't cognize this was expected to be hard. I fair picked an old problem, tried the most apparent item and threw several compute at it. Being a naive outsider power be a superpower now.

And I don't think this is really concerning archaeology. So far, most agentic AI stories have been concerning coding, math and physics, but it's much additional broad than that, and I anticipation this small example makes it a bit additional obvious. We are on the brink of a upheaval in all the sciences, and the lone bottlenecks remaining volition be curiosity and imagination (and compute).

At this point, we should likely lidar the entire earth and let the agents detect the rest.

Google updated its About brand settings for Search and Performance Max assistance document to explain how to oversee brands inside your brand list. Specifically, Google said, "Google's brand archive uses single, earth canonical brands fairly than idiosyncratic localized or local variants."

Google added this new paragraph to the document:

Google's brand archive uses single, earth canonical brands fairly than idiosyncratic localized or local variants. If you petition a localized brand and it remains "under review" or is unavailable for selection, hunt for and add the chief canonical brand name instead. Using the canonical brand ensures your targeting or elimination preferences use to all traffic pertaining to that brand.

I highlighted in red outline what Google added:

Brand settings authorize administration of tagged traffic using brand inclusions for Search campaigns or brand exclusions for Search and Performance Max campaigns. Brand inclusions restrict traffic to selected brands, during exclusions forestall ads from showing on particular tagged queries.

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GSC reports what Google really recorded: your clicks, impressions, and indexing issues. Semrush adds what GSC can't show, akin regular rankings, competitor data, and deeper backlink analysis. But connecting the two isn't a sole step. Each Semrush tool links to GSC on its own.

Below, we shield which Semrush tools nexus to GSC, how to set all one up, six ways to use the blended data, and how to fix average association errors.

If you're new to either tool, commencement alongside our Google Search Console guide, the Getting Started alongside Semrush guide, or our explainer on how Semrush data differs from GSC.

How Semrush and Google Search Console activity together

Semrush and Google Search Console activity together through distinct integrations in all Semrush tool (not one account-wide Semrush Search Console connection). You nexus your Google document to a Semrush project, afterward rotate on GSC inner all tool you desire to use.

Here's the data all tool pulls from GSC and anywhere you nexus it:

Site Audit is the one notable gap inside the Semrush Google Search Console integration set. It integrates alongside Google Analytics only, so comparing Site Audit results alongside GSC's indexing reports is a manual inspect (covered below).

Semrush additionally doesn't shop the data it pulls from connected Google accounts.

How to nexus Google Search Console to Semrush

To nexus Google Search Console to Semrush, nexus your Google document to a Semrush project, afterward nexus GSC inner all tool you scheme to use.

Step 1: Check that your GSC asset matches your project domain

Your GSC asset must equivalent the domain in your Semrush project, including www vs. non-www versions. If they don't match, Semrush can't discover the asset and volition display a "No properties equivalent your run scope" error.

If you scheme to use Backlink Audit, additionally inspect that the asset covers your base domain. A asset constricted to a subfolder, akin example.com/blog/, won't connect.

Step 2: Connect your Google document to your project

To nexus your Google account, open your project's SEO Dashboard, afterward click “Connect Google” in the top-right. You can additionally scroll to the chart, open the "Google Search Console" tab, and click "Connect."

Sign in alongside the Google document that has admission to your property, afterward choose the asset in the wizard. You don't need to nexus Google Analytics for GSC to work.

Once connected, the dashboard's "Google Search Console" tab shows your property's clicks and impressions complete time.

Step 3: Import GSC keywords into Position Tracking

To import keywords from Google Search Console into Semrush Position Tracking, use the "Import from..." list in the Keywords window:

• For new campaigns: Find it at the top-right of the Keywords stage or opening during setup
• For existing campaigns: Click the equipment icon in the top correct and choose "Tracked keywords" to open the Keywords window

Then, choose “Google Search Console” from the drop-down.

This adds the queries your location already gets impressions for, so you can track them daily. Review the imported catalog before you click "Add keywords to campaign" and "Save changes." Imported keywords figure toward your plan's keyword limit, which the opening shows below the list. 

For the remainder of the run setup, see Semrush's Position Tracking guide.

Step 4: Connect GSC to Backlink Audit

To nexus GSC to Backlink Audit, you provision a distinctive Semrush email location complete admission to your GSC property. This stage applies equal if you've already connected GSC elsewhere in Semrush.

Before you start, you need Owner or Full approval in GSC (restricted users can't complete this step) and a base domain property, as covered in Step 1.

Then, prosecute these steps:

1. Open your Backlink Audit campaign, click "Integrations" in the top-right, and click "Connect" under Google Search Console.
2. Click "Share access" next to your Google account. If no document appears, click "Add Google Account" and sign in first.
3. Copy the Backlink Audit email location shown in the window. Each project gets its own location in the following format: backlink.audit.{code}@gmail.com.
4. In GSC, go to Settings > "Users and permissions" > "Add user." Only verified asset owners can add users.
5. Paste the email location and choose "Full" permission. Backlink Audit needs complete approval to upload your disavow document to Google.
6. Back in Backlink Audit, click "Check association and Upload backlinks."

After you connect, Backlink Audit recrawls your overview alongside the new GSC links. A "Crawling links" advancement bar replaces the header buttons until the crawl finishes.

Step 5: Add GSC widgets to My Reports

To add GSC widgets to My Reports, open the study builder and drag them in from the Integrations sidebar. Find "Google Search Console" under SEO, afterward choose from two tabs:

• Widgets: Single metrics and tables, akin clicks, impressions, average CTR, average position, and breakdowns by query, page, country, device, or hunt appearance
• Groups: Prebuilt sets, including Search Performance Overview, Audience Insights, Desktop Search, and Mobile Search

If your Google document isn't connected yet, you can additionally nexus it from the widget itself. For a faster start, choice the Google Search Console template from the My Reports sidebar.

How to use Semrush alongside Google Search Console data

To use Semrush alongside Google Search Console data, commencement from what GSC shows concerning your own location (e.g.,  your genuine clicks, impressions, average positions, indexing issues). Next, use Semrush to inhabit in what GSC can't show: regular rankings, competitors, keyword difficulty, and your backlink profile.

SEO and AI hunt adviser Bill Widmer shares why he needs the two tools:

"I don't commencement an audit without read-only Search Console access. Semrush shows me anywhere a location stands against competitors, but lone GSC shows what Google really did alongside all page. On one client site, a checklist leaf was cited in AI answers for 50 prompts and motionless earned zero clicks from Google, and GSC's impressions and average stance are what inform you whether that's an indexing problem, a ranking problem, or a click problem. Semrush can't see your genuine clicks, and GSC can't see anyone else's site, so I never scheme an SEO scheme without both."

See ranking changes before they display up in GSC

To see ranking changes before they display up in GSC, import your GSC keywords into Position Tracking, which checks your rankings all day. GSC reports positions a few days late, and lone for days your leaf gets impressions, so a autumn can go unnoticed there for days.

That period postpone matters whenever rankings drop. When Bill tested this on his own site, Position Tracking flagged a 19-position autumn for "b2b saas ai hunt consultant," one of his most crucial keywords, on October 27. A day later, GSC's newest data was motionless from October 26, so the autumn wasn't apparent there yet. Only Semrush showed this autumn the day it happened.

While you’re in Position Tracking, add competitors to the identical run to see whether a autumn lone affected you or if it additionally affected all competitor on the hunt motor results leaf (SERP).

Vet striking-distance keywords

Vet striking-distance keywords (GSC queries that position approximately in positions 4 through 15) and inspect their difficulty and intent in Keyword Overview. Low-difficulty conditions whose intent matches the leaf that already ranks for them are your quickest wins.

For queries alongside elevated impressions but few clicks, run the chief term through the Keyword Magic Tool. If the connected conditions portion the page's intent, develop the existing leaf to contain those topics. If they item to a distinct need, create a new page. Both checks are manual, since Keyword Overview and the Keyword Magic Tool don't drag GSC data directly.

For the complete filtering method in GSC, see our guide to finding keywords in Google Search Console.

Clean up your backlink profile

Clean up your backlink overview by letting Backlink Audit merge GSC’s nexus data alongside Semrush’s own crawl, so you assessment the links Google reports alongside the ones Semrush found. With the two sources in one list, you’re small apt to young female a toxic nexus that belongs in your disavow file or a misplaced nexus value recovering.

Google lone recommends disavowing whenever spammy links have caused (or are apt to cause) a manual action, according to Google’s Search Console Help. For most sites, the bigger payoff is recovering damaged and misplaced links.

When Bill connected GSC on his own site, referring domains rosy from 984 to 1,090, and nearly all of the new ones were flagged toxic (222 to 324). Part of that came from nine days of regular crawling, but it’s a logic to assessment new domains before you disavow anything.

If you do need to disavow, build one catalog in Semrush, import your existing disavow records from GSC, and upload the blended catalog to Google’s Disavow Tool. Our guide to links in Google Search Console covers what GSC’s own links study shows.

Check whether ranking gains turned into clicks

Check whether ranking gains turned into clicks by comparing Position Tracking alongside the GSC diagram on your SEO Dashboard. Both sit on the identical page, so you can see rankings and genuine clicks without switching tools.

If rankings increase but clicks remain flat, assessment the SERP for those keywords. A featured snippet, AI Overview, or feeble heading tag can keep searchers from clicking equal whenever you position well. 

GSC's generative AI achievement reports display how frequently your pages appear in AI Overviews and AI Mode, which can assistance explain the gap. For additional on study these signals together, see our guide to measuring SEO results.

Build client reports alongside GSC data

Build client reports alongside GSC data by placing GSC widgets next to Semrush widgets in the identical My Reports PDF. That way, clients see Google's own clicks and impressions alongside rankings, competitors, and backlink data.

Try pairing a GSC Query array next to Position Tracking rankings for the identical keywords. Together, they display clients how they position and how much traffic those rankings really bring.

Diagnose indexing issues alongside Site Audit

Diagnose indexing issues by checking GSC's leaf indexing study first, afterward operating Site Audit to discover the URLs and causes rearward them. GSC tells you what Google has really flagged, so fix those issues first.

Site Audit crawls your location the way a hunt motor would. It shows which pages trigger all issue and catches issues Google hasn't reported yet, akin damaged inner links or redirect chains.

How to fix average GSC association errors

To fix average GSC association errors, inspect three things: 

1. Whether your asset matches your project
2. Whether Backlink Audit's email location has Full permission
3. Whether Semrush has already synced all nexus it can

"No properties equivalent your run scope"

This error method the Google document you connected doesn't have a asset that matches your Semrush project domain. It normally happens whenever you're signed in to the incorrect Google account, or whenever the asset and project differ on www vs. non-www.

To fix it, sign out of Google, sign rear in alongside the document that owns the matching property, and nexus again.

"Make certain you have added document … to your GSC properties"

This Backlink Audit error method GSC doesn't acknowledge the Backlink Audit email address. The location may be missing, added alongside "Restricted" alternatively of "Full" permission, or copied from a distinct project.

To fix it, copy the location again from "Integrations" in the current campaign, afterward re-add it in GSC alongside "Full" permission. Each project has its own particular address, so one from another project won't work.

"All links were uploaded from shared properties"

This Backlink Audit communication isn't a failure. It method Semrush has already pulled all backlink accessible from the properties you shared and volition keep syncing them periodically.

To get additional backlinks into the audit, nexus another Google document that has a asset matching your run scope.

FAQ

Google Search Console vs. Semrush: what's the difference?

Google Search Console reports Google's own data, but lone for sites you own or manage. Semrush covers any domain, so it adds competitor research, position tracking, keyword research, and backlink inspection on top. In a Semrush vs. Google Search Console comparison, neither replaces the other: one shows what happened on your site, and the another shows the market about it.

Why don't Semrush and GSC display the identical traffic numbers?

GSC counts the genuine clicks your location got from Google. Semrush estimates traffic from the positions a location ranks in, the monthly hunt quantity of those keywords, and average click-through rates per position, according to Semrush's glossary. Different numbers are normal and don't average your association is broken.

What admission do I need to nexus Google Search Console to Semrush?

The Google document you nexus to Semrush must have admission to your GSC property. Backlink Audit requires Owner or Full permission, and a verified owner must add its distinctive email location as a user.

Read next

Zig keeps improving as a programming language. Unfortunately, its administration increasingly seems resolute to create it a bad choice for the group who really desire to use it.

Zig is a fascinating programming language. Its scheme contains genuinely fine ideas, its specialized ambitions are impressive, and it has attracted gifted developers who have spent years construction a grave systems programming toolchain. And notwithstanding all that, it's becoming increasingly difficult to propose it.

The issue is that Zig appears to be poorly managed.

Over the former year, the project has made a sequence of decisions that individually power appear akin average disagreements concerning open-source development, during together revealing a deeply concerning pattern.

The tongue continues to interrupt compatibility without a tangible dedication to stability. Its contribution guideline excludes alternatively lawful engineering workflows. One of its most notable users abandoned the language, lone to be met alongside community hostility. And its move distant from GitHub replaced a mature betterment phase alongside a smaller, small proven assistance beset alongside reliability issues, whose operators increasingly enforce their own philosophical preferences on hosted projects.

The recurring theme is that Zig's maintainers appear additional curious in enforcing their imagination of how application have to be developed than making Zig a applicable tool for group who desire to build software. And that is a grave issue for a programming language.

Zig is constantly unfinished, and its users pay the price

Zig has been under betterment for additional than a decade. It remains at type 0.x, without a resolute date for a stable 1.0 release.

There is nothing inherently incorrect alongside a lengthy betterment sequence or a type figure below 1.0. Plenty of helpful application operates under so-called 0ver. esbuild is one notable example of rock-solid, uncompromising norm and small breakage notwithstanding its important type remaining at 0.

But Zig is a programming language, not a bundler.

A programming tongue is infrastructure. Developers compose applications and libraries against its syntax, norm library, compiler, and build tooling, and those interfaces decide how much of the surrounding ecosystem remains usable from one publish to the next. In Zig, they appear to be everlastingly changing, alongside the authors nigh-obsessed concerning breakage.

Consider the 0.15.1 publish notes, alongside significant changes to the I/O interfaces and norm library, including the lovingly named ‘Writergate’. It's amusing adequate if you're penning the compiler, presumably small if you're the one whose code no longer compiles. Version 0.16 continued the transition, and 0.17.0, released in October 2026, brought additional changes to the tongue and its tooling. It nearly appears as although the Zig maintainers enjoy breaking compatibility; if not, they certainly appear comfortable alongside it.

Thus upgrading Zig is frequently not fair a matter of installing a new compiler. It frequently method rewriting significant parts of existing code, updating dependencies, and waiting for tools and libraries to capture up. The fact that Zig openly documents these changes doesn't lessen the burden.

Some of these changes may or may not enhance the design; but that's beside the point. Every alter additionally creates activity for everyone who has already built item on top of the language. And following additional than ten years, it's additional than sensible to ask whenever this stops and we can commencement getting grave concerning penning Zig code¹.

The maintainers have a roadmap to 1.0, but that gives developers no meaningful sign of whenever they can anticipate the interfaces they depend on to halt changing. It may fine obtain another decade.

That leaves them alongside a choice: freeze their projects on old compiler versions or often expend engineering attempt keeping up alongside upstream. When a project offers you two bad choices, you cognize item isn’t going well.

A programming tongue cannot remain a perpetual test without consequences. At several item experimentation stops being a advantage and starts becoming a burden on everyone using the language. At this stage, we are way former that point.

Zig's instability isn't merely an inconvenience. It is among the chief reasons the tongue remains difficult to depend on.

And it makes the maintainers' outlook toward contributors considerably harder to defend.

A project that wants to instruct you a instruction alternatively of accepting your code

Zig has an exceptionally restrictive AI contribution policy.

No AI-generated code. No AI-assisted editing. No AI-assisted translation. No brainstorming alongside an LLM and afterward sharing the result. Not equal using an LLM to discover bugs. Really?

There's an apparent difference between dumping a thousand lines of unreviewed AI slop into a drag petition and using an aide to comprehend an unfamiliar part of the compiler, perchance discover a bug, or assistance execute a fix. And Zig's guideline makes remarkably small attempt to differentiate between the two.

In Contributor Poker and Zig's AI Ban, the maintainers explain their philosophy. They desire to allocate in contributors who volition create a profound understanding of Zig and remain engaged complete time. Reviewing low-quality drag requests is expensive, and AI has allegedly made that issue worse.

Fair enough. Nobody wants to assessment garbage. But afterward comes the revealing part:

"You perform the person, not the cards."

The part argues that investing in the correct contributor matters additional than the contents of their archetypal drag request. It equal acknowledges that capable AI-assisted contributors exist, but considers them an unattractive bet compared alongside group who don't use these tools.

Let’s regard what that means: A helpful contribution can (and certainly must, in the case of Zig) be rejected not since it's incorrect, poorly tested, or unmaintainable, but since its author used the incorrect tools to create it. The project is no longer judging lone the contribution. It's judging the contributor's betterment process. Merit solitary is no longer enough.

And apparently, contributors are expected to rotate into deeply acquainted alongside Zig's internals to acquire the privilege of fixing something. Why?

Not everyone wants to rotate into a compiler engineer. Some group encounter a bug during trying to build their own application and would merely akin to fix it. That's a absolutely lawful contribution whether you average to activity on Zig for the next ten years or never contact it again.

The really ridiculous part is how this outlook clashes alongside Zig's imperishable instability. The language, the norm library, the build scheme continually change. Developers have to study the new interfaces, migrate their projects, update their dependencies, and afterward reiterate the exercise whenever the next publish arrives.

And during imposing this uninterrupted relearning exercise, Zig's maintainers have decided that certain tools which power create it easier are unacceptable.

You must comprehend the language. You are to keep up alongside its changes. You must study the internals. And you'd improved do it the approved way. At what item does maintaining your Zig code commencement being application betterment and halt becoming homework assigned by the Zig project?

And how does this live up to Zig's own promise to ‘focus on debugging your use fairly than debugging your programming tongue knowledge’?

I initially got curious in Zig exactly since of that promise. Whenever I looked at Rust code, I felt akin I needed a PhD to get it. Zig was straightforward and uncomplicated to understand. That was long before they cut in affection alongside breaking for the purpose of breaking.

Most developers don't use programming languages since they desire an ongoing instructional association alongside the compiler's maintainers. They fair desire to get material done, alongside the correct tools.

It's absolutely sensible to petition that contributors comprehend and remain rearward their work. It's far small sensible to dictate and prescribe how they get that understanding. A compiler betterment doesn't rotate into worthless since an LLM helped create it. Rejecting helpful activity on that basis is not specialized rigor.

It's dogma.

Bun remaining Zig. And Zig's reply was embarrassing.

For years, Bun was practically liberated promotion for Zig. A high-performance JavaScript runtime, written in Zig, attracting important acceptance and showing that the tongue could be used for grave manufacturing software. Bun held up the Zig banner proudly. Bun was treating Zig as important 1 even as Zig slipped additional and additional into the abyss of eternal breakage.

Then, in July 2026, Bun announced that it had rewritten its Zig codebase in Rust.

Bun had lawful specialized reasons. Its squad described recurring memory-safety problems, particularly about the communication between JavaScript's refuse collector and Zig's manual recollection management. Rust's ownership scheme offered guarantees that were precious for Bun's workload.

To this end, Bun had additionally been maintaining its own modifications to the Zig compiler, alongside upstream contribution conflict adding to the care burden. This should have been alarming to the Zig project, although it cut on deaf ears.

One of its most notable users, alongside important engineering resources and years of funding in the language, had concluded that continuing alongside Zig wasn't value it. And well, fair: not everyone must choose Zig, nor commit to it in perpetuity. But whenever a important person walks away, perchance it's value asking why, or in this case merely peruse the reasoning. Instead, the project responded alongside a spectacularly caustic community attack.

In a lengthy reply to Bun's rewrite, Zig's guidance went fine beyond disagreeing alongside Bun's specialized arguments. The article criticized Bun's engineering practices, management, betterment culture, and endeavor decisions. It described the basis as relieved that the association was complete and treated Bun as a liability (a term the Zig project seems particularly eager whenever justifying its caprices). And all this in reply to a project that had spent years using Zig, publically promoting it, contributing to its ecosystem, and donating significant amounts of prosperity to its development.

Bun's notice was broadly appreciative of Zig. Zig's reply was item but appreciative. The article was afterward revised and acknowledged resentment in its before framing. That's something, but it doesn't undo the damage that reveals immaturity and a serious deficiency of understanding on the part of the project maintainers of the dynamics of application development.

A programming tongue does not own its users.

Bun was under no duty to keep using Zig merely since the maintainers believed in its design. It wasn't obligated to create application according to Zig's preferred engineering culture, either. If its needs were improved served by Rust, that's Bun’s decision. It didn’t equal need to validate itself, but the fact it did showed grace towards Zig and maturity towards its own users, two qualities the Zig project seems seriously lacking in.

And if Zig's maintainers disagreed alongside Bun's specialized reasoning, they could have made that disagreement without turning it into a community disagreement concerning the group and civilization rearward Bun.

This sends a particularly concerning communication to everyone else. If a project as notable as Bun gets this benevolent of care on the way out, what should smaller projects anticipate whenever they condemn Zig or decide to move on? This is a far cry from how you build confidence in a programming language. In fact, it's exactly how you create group reluctant to depend on it. Nothing inspires confidence in a project's governance fairly akin observing it throw a community outburst since one of its users decided to leave.

Bun's immigration solitary doesn't demonstrate Zig is a bad language.

But the way Zig handled losing Bun says plentifulness concerning the project's management.

Codeberg was a awful choice

In November 2025, Zig announced its immigration from GitHub to Codeberg.

In and of itself, leaving GitHub isn't the problem. GitHub has plentifulness of problems of its own, and Zig's complaints concerning GitHub Actions were lawful reasons to regard alternatives. The issue is anywhere Zig went instead.

Codeberg is a small nonprofit Git hosting service. It's also, in my experience, frustratingly unreliable. I've often encountered association issues or established it unavailable whenever I needed it.

And I'm far from the lone one complaining.

When person suggested moving CachyOS from GitHub to Codeberg, a attendee in the CachyOS forum discussion responded:

"Codeberg frankly is notoriously unstable and tends to have association issues often."

Other participants raised much the identical concern, alongside one arguing that Codeberg hadn't yet matured adequate to be relied on as a grave GitHub replacement.

These are merely anecdotal person reports. But they equivalent my experience, and reliability isn't several obscure characteristic that lone matters to unusually demanding users. It's the naked minimum for a betterment platform.

Zig didn't need to remain on GitHub. It could have chosen GitLab, evaluated another established hosting providers, or run its own Forgejo instance. It power have the specialized cognition to do so, but it evidently preferred not to obtain on that work. Self-hosting comes alongside work, obviously. But if controlling your own infrastructure is so important, perchance that is the benevolent of activity value doing.

Instead, Zig moved crucial betterment infrastructure to a provider alongside questionable reliability and tried to market it as progress. And the reliability concerns aren't equal the worst part.

In July 2026, Codeberg published "Protecting our FLOSS commons from LLMs," explaining its decision to restrict certain categories of AI-heavy projects.

Codeberg has all correct to oversee its resources, forestall abuse, and defend itself from automated crawlers. But the notice doesn't halt there. It makes sweeping judgments concerning the legitimacy of LLM-based betterment itself. Its operators explicitly contend that LLMs are incompatible alongside liable open-source application development, and its policies may exclude projects on the basis of how their code was produced.

That's a beautiful bonzer direction for a Git hosting provider. As another developer puts it in "I Regret Migrating to Codeberg," the involvement isn't fair which projects are currently restricted. It's that the hosting provider has taken it upon itself to decide which alternatively lawful forms of application betterment pertain on its platform.

You don't have to akin AI to see the issue here. Codeberg calls itself a residence for liberated and open-source software, yet increasingly seems curious in deciding which kinds of application betterment deserve to be welcome. Apparently the commons is lone average so lengthy as everyone agrees on how to use it.

A Git forge should presenter code, oversee issues, and facilitate collaboration. It should enforce sensible limits and forestall abuse. It shouldn't need to continue judgement on whether a project's betterment doctrine is sufficiently virtuous.

Codeberg's notice came following Zig's migration, so it doesn't established what the maintainers knew at the time. But it certainly doesn't create their choice appearance any improved in hindsight.

Zig traded GitHub's problems for a distinct set of problems, alongside worse reliability in my cognition and a hosting provider increasingly concerned alongside policing how application ought to be written.

And since the project's own policies align alongside that philosophy, I discover it difficult to accept applicable considerations were the lone logic for the choice.

Apparently equal donations can be the incorrect kind

The GitHub immigration announcement additionally contained a particularly different passage concerning GitHub Sponsors.

After acknowledging how crucial the phase had been to Zig's fundraising, the maintainers wrote:

"Although GitHub Sponsors is a ample fraction of Zig Software Foundation’s contribution income, we regard it a liability."

They afterward asked supporters to move their recurring donations to Every.org and announced the withdrawal of GitHub Sponsors perks. Again I must ask, why?

Zig is funded substantially through donations. People voluntarily provision the project prosperity so that its developers can keep operating on the language. And now among the platforms through which group have been supporting it is apparently a liability? Yesterday an asset, today a liability?

Granted, relying on a third-party phase carries risks. GitHub Sponsors could alter its conditions tomorrow. But that's a logic to diversify your fundraising, not discard an existing channel. Why not depart the repository on GitHub as a read-only mirror and keep accepting donations there?

Why should group who are already supporting Zig have to alter their fee arrangements fair since the maintainers no longer akin GitHub? The genuine betterment could happen entirely on Codeberg. Nobody would have to use GitHub for issues, drag requests, or CI. And the group who favor GitHub Sponsors could continue donating through it.

Perhaps there were administrative reasons for wanting to consolidate fundraising elsewhere. But the notice doesn't established why keeping the two channels was unacceptable. The maintainers did appreciate their existing supporters, and they weren't exactly refusing donations. But I motionless discover the communication remarkably tone-deaf.

When your project relies on voluntary contributions, making it small convenient for group to assistance you should necessitate a improved clarification than disliking the phase they happen to use.

It's one item to refuse to depend on GitHub for development. It's another to rotate that penchant into unnecessary conflict for group who are trying to provision you money.

Once again, everyone alternatively is expected to modify Zig's principles.

Going nowhere

Zig isn't dead. Development continues, and the compiler keeps getting better.

But specialized advancement isn't the identical item as maturity. What fine is an increasingly advanced programming tongue if using it remains an exercise in keeping up alongside breaking changes and the whims of project management?

Look at the pattern. Users are expected to keep up alongside an unstable language. Contributors are expected to activity the approved way. A notable downstream person decides Rust fits better, and the project publically lashes out. The hosting phase changes, and donors are asked to move alongside alongside it.

Zig's maintainers anticipate everyone alternatively to modify to the project during showing amazingly small willingness to modify the project to everyone else.

Undoubtedly they have the correct to run Zig nevertheless they want. Open origin doesn't average maintainers owe everyone unlimited support, liberated code review, or eternal backward compatibility. But the reverse is likewise true: users don't owe maintainers their time, loyalty, or accord alongside their philosophy.

A developer wants to compose software, not expend countless hours migrating standard-library APIs. A contributor power desire to fix a bug, not demonstrate allegiance to a sanctioned academy of engineering. A business wants a tongue that solves its problems, not a address concerning how to run its business. And a contributor wants to assistance a project, not get embroiled in its disagreement alongside GitHub.

Good project administration method making compromises. Sometimes that method putting stability onward of another elegant redesign. Sometimes it method accepting a helpful place from person who doesn't portion your preferred betterment ideology. Sometimes it method swallowing your pride whenever a important person moves on. And sometimes it method choosing boring, dependable infrastructure alternatively of making a philosophical statement.

Zig seems resolute to do item but that.

None of this is inevitable. Zig could keep its specialized ambitions during becoming a much additional dependable tongue and a much easier project to activity with. But that would necessitate its maintainers to commencement treating users' needs as item additional than an inconvenience to be managed.

The project can keep refining its tongue scheme forever. It can keep insisting on its own idea of fine engineering, rejecting the incorrect kinds of contributions, and choosing platforms that align alongside its worldview.

But at several point, it needs to decide whether it wants to build a programming tongue group can use or an ongoing test in how its maintainers think application have to be developed.

Zig has spent additional than a decade figuring out what its maintainers desire the tongue to be. Perhaps it's period they started caring a small additional concerning what its users need it to be.

Until then, the Zig project is going nowhere.

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