DEV Community: Vin Patel

Claude Code's AGENTS.md Fallback Ends A Quiet Format War

Vin Patel — Sat, 19 Sep 2026 07:13:33 +0000

Originally published at vinpatel.com

Everyone reading Claude Code's new AGENTS.md fallback as a compatibility footnote is missing the real story: Anthropic just admitted OpenAI's file format won.

The change itself is small. Claude Code will now read AGENTS.md when a project has no Claude.md. One line in a changelog. But changelog lines are how format wars actually end — not with an announcement, but with a quiet fallback rule that nobody bothers to argue about.

Look at the sequence.

In 2025, OpenAI's Codex agent popularized AGENTS.md as the standard file for repo-level instructions to coding agents — the equivalent of a README, but written for a model instead of a human.

Later in 2025, other agentic coding tools started recognizing AGENTS.md alongside their own proprietary config files, treating it as a lowest-common-denominator format any agent could parse.

In 2026, Claude Code's own changelog joined them, adding AGENTS.md as the fallback whenever Claude.md is absent.

The through-line is not "Claude Code added a feature." It's that Anthropic just conceded a naming war it never publicly entered. Claude.md was Anthropic's own convention, shipped with Claude Code from the start. AGENTS.md was a rival's. When Claude Code decides to read the rival's file instead of asking developers to migrate to its own, that is not interoperability. That is surrender dressed as convenience.

For anyone maintaining a multi-agent repo — Claude Code here, Codex there, a third tool next quarter — this matters more than the changelog entry suggests. You no longer need three instruction files split across three formats. One AGENTS.md, read by all of them, with Claude.md only necessary if you want Claude-specific overrides. That collapses a real maintenance cost: the drift between config files that inevitably fall out of sync the moment someone updates one and forgets the other two.

The practitioner read: this is the first crack in what could become a genuine standard. If Claude Code treats AGENTS.md as the default and CLAUDE.md as the override, expect Anthropic's own documentation to start recommending AGENTS.md-first setups by the end of 2026, not just supporting it quietly as a fallback. If instead Claude.md remains the primary recommendation in Anthropic's own docs a year from now, this dispatch is wrong and the fallback was just compatibility theater.

If you're already running Claude Code with skills, subagents, and plugins, the file convention matters less than what you put inside it — Claude Code as a Daily Driver covers the setup choices that actually move output quality. And if you've been tracking whether Claude Code's output has changed under the hood, the quality reports breakdown is worth a read before you assume the model is the variable.

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Bend Says Proof Stops AI Coding Mistakes. Reality Is Thinner.

Vin Patel — Fri, 18 Sep 2026 07:16:00 +0000

Originally published at vinpatel.com

Everyone is reading Bend's launch as another shot at painless parallel programming. It is actually a bet that mathematical proof, not the AI's word, is what should stop AI-written code from shipping broken.

The tagline on Bend's own site says it plainly: a language that blocks AI mistakes via proof, on CPU and GPU. That is the claim, made by the people building the thing, not a reviewer's gloss on it.

What is actually shipped today is a language and a runtime. Code written in Bend runs on CPU and on GPU without the developer hand-writing thread management, and the type system is pitched as the mechanism that catches mistakes before the program ever executes. That is real, and it is not nothing. What is not shipped, at least not on the page making the claim, is any independent measure of how many AI-generated mistakes that proof layer actually catches, or how it performs against a production codebase written by a coding agent rather than a human.

The gap exists because "proof" only covers what the type system can express. Memory safety, type mismatches, and certain classes of concurrency bugs are provable. A hallucinated API that compiles cleanly, a function that does the wrong thing for the right types, a business rule an agent quietly reinterpreted — none of that trips a type checker. Most of what breaks in AI-written code lives in that second category, not the first. A language can be provably memory-safe and still ship logic that nobody asked for.

Closing that gap takes evidence nobody has published yet: independent teams running real coding-agent output through Bend and reporting what got caught versus what slipped through, plus enough production adoption that the claim gets tested by people with no reason to flatter it. Until that exists, the honest read is that Bend has a real technical idea — proof over vibes — attached to a claim that is currently self-reported.

That's also why guardrail work elsewhere is worth watching closely: teams bolting verification onto existing agent output, rather than shipping a new language, are already publishing what gets caught and what doesn't. The Forge guardrails writeup is one example of that kind of accountability, and it's a useful contrast to a homepage pitch with no third-party numbers behind it yet. If you're stitching agents into a real build pipeline, the full agentic SDLC breakdown covers where verification actually needs to sit in that stack.

Bend's idea deserves a real test, not just a homepage. Subscribe at vinpatel.com/subscribe/ to get told the moment someone runs that test and publishes the result.

A 4B Model Just Beat Postgres's Query Planner by 81%

Vin Patel — Thu, 17 Sep 2026 07:13:04 +0000

Originally published at vinpatel.com

A 4B model just beat Postgres's own query planner — producing plans that run 81% faster. That is a genuine speed win over software Postgres has spent decades tuning.

The claim comes from a project called Qorl, posted to Hacker News under the title "Training a 4B model to produce 81% faster query plans than Postgres." The pitch: a 4-billion-parameter model, trained to generate execution plans, outpaces the cost-based optimizer that ships inside every stock Postgres install — the same component that decides join order, index usage, and scan strategy for every query your application runs.

This matters most to anyone running Postgres in production and feeling the tax of its planner on complex joins or analytical workloads: backend engineers at scale-up startups, data infrastructure teams, and indie founders who chose Postgres because it is free and everywhere, then hit its ceiling once query complexity grew. It also matters to anyone building tooling on top of small models, because this is a case where a 4B model — small enough to run cheaply, not a frontier model — is being pointed at a narrow, high-value systems problem instead of general chat.

The mechanism is the interesting part. Postgres's planner works by estimating costs at query time, walking through statistics and heuristics before picking a plan. A trained model instead learns, ahead of time, what a good plan looks like for a given query shape and skips that live search. That trade — replacing a hand-tuned heuristic search with a model that has already seen enough queries to pattern-match a fast plan — is the same idea behind learned query optimizers researchers have chased for years. What is new here is the size: a 4B model is small enough to run inline, not as an offline research artifact.

What this does not mean yet: Postgres has not shipped this. There is no patch, no extension, no production deployment described. It is a benchmark result on Hacker News, not a merge into Postgres core. The open question for anyone tempted to act on it is whether this generalizes past whatever workload it was benchmarked on, and how it behaves when query shapes it was not trained on show up. Builders who work with small models on narrow systems problems will recognize this pattern from Show HN: Forge — a small model, aimed tightly, closing a gap that used to need something bigger. If you are stitching model-in-the-loop systems into production tooling, the shift toward agentic pipelines doing this kind of decision-making live is covered in The Autonomous Stack.

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Crunchbase's Sector Snapshot Shows AI Eating Sales And Marketing Funding

Vin Patel — Wed, 16 Sep 2026 11:25:35 +0000

Originally published at vinpatel.com

Crunchbase's own sector snapshot puts it in writing: AI has become the default filter for sales and marketing startup funding, not a feature bolted onto a pitch deck after the fact.

That's the artefact. Crunchbase didn't publish a hot take about AI hype in go-to-market software. It published a "Sector Snapshot" — the kind of report that tracks where check size and deal count are actually moving across ecommerce, SaaS, and CRM tooling. The headline isn't marketing copy. It's a category label a data team assigned after looking at where the deals landed.

That distinction matters more than it sounds. Plenty of startups slap "AI-powered" on a sales deck because it's what investors want to hear right now. Crunchbase's framing says something different: enough of the actual capital flowing into sales and marketing infrastructure is now tied to AI capability that the category itself is being redrawn around it. The report is a claim about where money is moving. It is not a claim that AI improves conversion rates or shortens sales cycles. Those are two different statements, and conflating them is the fastest way to misread this story.

For anyone building sales or marketing tooling without an AI layer, the implication is blunt. The comparison set has shifted. A CRM add-on or outbound tool now gets evaluated against AI-native competitors by default, whether the founder wanted that comparison or not. Raising on a non-AI GTM thesis just got a harder pitch to make — not necessarily because AI tools work better, but because the money is already sorting itself that way. If you're one of the small teams trying to compete against funded, AI-native GTM stacks without matching their headcount, the Solo Founder Sales & Marketing Atlas is built for exactly that gap — how tiny teams sell like companies many times their size.

What the snapshot doesn't settle is whether this shift reflects founders building genuinely better sales and marketing products, or investors relabeling familiar SaaS categories to match whatever capital is currently fashionable. Crunchbase tracked where the money went. It didn't audit whether the AI inside these products is doing real work or just doing the work of getting funded. That's the open question every operator evaluating an "AI-powered" sales tool should be asking before they buy one or build one.

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ZGCM-1 Claims Full Openness. Here's What That Actually Requires

Vin Patel — Tue, 15 Sep 2026 07:11:35 +0000

Originally published at vinpatel.com

Everyone skimming ZGCM-1's title reads 'fully open and extremely efficient' as boilerplate. It isn't. It's a checkable claim nobody outside the authors has checked yet.

The claim, stated exactly as the paper makes it: ZGCM-1 is "a fully open and extremely efficient foundation model for math and agentic search." Two absolutes doing a lot of work — fully, extremely — sitting right there in the title of an arXiv preprint.

Here's what's measurable right now, this week, from outside the lab that built it: nothing yet. There's no independent benchmark run attached to this listing. No third party has taken ZGCM-1 and pointed it at a math or agentic-search task the authors didn't select themselves. The efficiency claim, whatever it eventually turns out to be worth, currently rests entirely on numbers the authors chose to report, measured on evaluations the authors chose to run. That's the honest state of any paper in the hours and days after it lands on arXiv, math-focused or otherwise.

The gap exists because of timing, not dishonesty. Peer review and independent reproduction take weeks or months. Publishing a preprint takes a day. "Fully open" is also doing more work than it looks like — it can mean the weights are downloadable, or it can mean the weights, the training data, and the training code are all downloadable. Those are two very different releases wearing the same adjective, and the title doesn't tell you which one you're getting. You have to go read the release section of the paper itself.

What would actually close the gap: someone outside the author list running ZGCM-1 against a math or agentic benchmark it wasn't built to win, then publishing that result next to the paper's own numbers. Until that happens, "extremely efficient" is a well-produced hypothesis, not a result. The same test applies to "fully open" — check whether the data manifest shipped, not just the inference weights. A release that hands you weights and a PDF is open the way a locked car is open when you can see the engine through the window.

If you're weighing ZGCM-1 for a math-heavy or agentic pipeline this week, skip the abstract and go straight to the artifacts section. That's where "fully open" either survives contact with your own repo or doesn't. Builders who've run this cycle before with agentic tooling know the pattern: the paper's own benchmark is rarely the one that matters once a model is actually running in production, which is the same lesson buried in how guardrails changed one small model's real agentic reliability — the gap between a reported number and a working system is the whole story.

The number that will actually tell you whether ZGCM-1 earns its title hasn't been published by anyone outside its own authors yet. Watch for that one, not the one on the arXiv page. Stories like this land daily — subscribe at vinpatel.com/subscribe/ to get the next one before the hype cycle catches up to the reproduction.

Why Insight Partners Is Betting Against the OpenAI Consensus

Vin Patel — Mon, 14 Sep 2026 07:19:08 +0000

Originally published at vinpatel.com

Every major venture dollar in AI is stacking on two names: OpenAI and Anthropic. Insight Partners is doing the opposite, and it's saying so out loud.

Managing director Deven Parekh laid out the firm's reasoning in a new TechCrunch interview, arguing that Insight is deliberately spreading its AI bets across the stack instead of concentrating capital in the two dominant foundation model labs that most of the industry now treats as the only trade worth making.

That decision matters for anyone raising money outside the OpenAI/Anthropic orbit right now. Founders building infrastructure, developer tooling, vertical applications or alternative model providers have spent the last stretch of this cycle watching a huge share of venture attention funnel toward a handful of frontier labs. If the dominant funds only write checks toward those two names, the rest of the ecosystem gets starved of capital regardless of how good the product is. Insight's public stance signals that at least one large, active investor is explicitly not playing that game.

The mechanism here is straightforward portfolio math, not contrarian posturing for its own sake. A fund that concentrates its AI exposure in two companies is making a bet on two valuations, two product roadmaps and two sets of leadership decisions. Any stumble in either name — a model that underperforms, a pricing shift, a regulatory hit — hits the whole vintage at once. Spreading capital across infrastructure, tooling, applications and other model layers means no single company's bad quarter can wreck the fund's returns. It also means Insight can still win big if the value in this cycle ends up accruing somewhere other than the two labs everyone else is chasing.

For builders, the read is simple: the money is not actually as consolidated as the OpenAI-and-Anthropic headlines make it look. There is still serious capital looking for exposure to the rest of the stack. If your product lives in that rest-of-the-stack layer, this is evidence, not wishful thinking, that big funds are willing to back you instead of waiting for the next foundation model raise.

If you're building in that layer, the piece on building end-to-end products in full agentic mode and the breakdown of what SpaceX's IPO means for builders are both worth your time this week. Want more reads on where AI capital is actually moving? Subscribe at vinpatel.com/subscribe/.

Why Real-SWE Tests Coding Models On Your Own Codebase

Vin Patel — Sun, 13 Sep 2026 07:07:47 +0000

Originally published at vinpatel.com

By the end of this you'll know how to build a private benchmark that tells you whether a coding model actually works on your codebase, not someone else's GitHub history.

This matters right now because a company called Specific just shipped Real-SWE, a benchmark that scores AI models against private, real-world, enterprise codebases instead of the public repos every other leaderboard scrapes. That distinction is the whole story: a model that tops a public benchmark has often seen those exact repos, those exact bugs, those exact pull requests during training. Your codebase is not on the internet. No amount of leaderboard glory tells you how a model performs against code it has never seen, written in your patterns, with your conventions, touching your test suite.

Here's the recipe Real-SWE's approach implies, adapted for your own repo:

Pull a handful of real, already-merged pull requests from your private codebase.
Strip each one down to just the issue description and the code state before the fix.
Hand the model only that starting state and the issue, nothing else.
Ask it to produce a patch, then run your actual test suite against it, no partial credit.
Score pass or fail against your own tests, not against a rubric a lab wrote for itself.
Re-run the same set every time you swap models or upgrade a version, so the comparison stays apples-to-apples.

Here's a starting prompt template you can paste into your eval harness today:

CONTEXT: [paste the repo state before the fix, relevant files only]
ISSUE: [paste the original issue description, unedited]
TASK: Produce a patch that resolves the issue above.
CONSTRAINT: Do not modify test files.
OUTPUT: A unified diff only.

Run that same block against every model you're evaluating, then run your real test suite against whatever diff comes back. That's the entire mechanism Real-SWE is built on, and you don't need their infrastructure to start doing it yourself this week.

The gotcha: a model that looks strong on public leaderboards can quietly underperform on your repo, and you will not find out until you test it against your own code. Public benchmark scores are not a proxy for your codebase. Treat them as marketing, not evidence, until you've run your own private set.

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Garry Tan's Distillation Call Admits US Open-Weight Labs Are Behind

Vin Patel — Sat, 12 Sep 2026 07:11:33 +0000

Originally published at vinpatel.com

Everyone reads Garry Tan's push for US open-weight labs to distill frontier models as cheerleading. It's an admission: the word "too" means everyone else already does this, and the US doesn't.

The tempo of that admission matters more than the ask itself.

In 2023, the open-weight movement treated distillation as a workaround for labs without frontier-scale compute — a way to compress a bigger model's behavior into something smaller you could actually run.

By 2025, distillation had stopped being a workaround and become the default strategy. Compressing a frontier reasoning model into a smaller student turned "train small on a big teacher" into the playbook that open-weight labs without a frontier training budget started copying wholesale.

On September 11, 2026, Garry Tan, Y Combinator's president, told US open-weight labs to do the same thing everyone else already had. Not as encouragement. As a correction.

The through-line: distillation went from a cost-saving trick to the only viable lane for labs that can't afford to pretrain frontier models from scratch. Everyone building outside that frontier tier — in the US and out of it — is now choosing between two paths. Raise enough capital to compete on pretraining, or admit you're in the distillation business and get good at it fast. Tan's post reads like someone watching the second group get outbuilt by labs elsewhere that made peace with that choice earlier.

That's the part worth sitting with if you're shipping on top of an open-weight model right now. The lab whose weights you depend on isn't really choosing between "open" and "closed." It's choosing between distilling someone else's frontier model well, or falling behind labs that already did. Which strategy your model's lab picked is now a more useful signal than its benchmark scores.

Here's the falsifiable version: if Tan's push actually moves anyone, expect at least one US open-weight lab to publicly announce a distilled model built on a named frontier teacher before the end of 2026. If none does, the "too" in his post was accurate — and stayed that way.

For the broader arc of how the field got from raw scaling to this kind of triage, the three years that changed everything is worth the read. And for a sense of how uneven "frontier" performance actually is once you stop trusting the benchmark, this look at where a flagship model actually fails is the companion piece.

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Can Mathematicians Still Trust OpenAI With Unpublished Proofs?

Vin Patel — Fri, 11 Sep 2026 07:10:12 +0000

Originally published at vinpatel.com

Can a mathematician still hand OpenAI an unpublished proof without regretting it? Researchers are asking that question again, and the timeline shows why the answer keeps getting worse instead of better.

November 2024: Epoch AI launches FrontierMath, a set of unpublished, expert-level math problems built specifically so no model could have memorized the answers from its training data. Research mathematicians are recruited to contribute problems on the understanding that the set stays genuinely unseen by any lab.

December 2024: mathematicians learn that OpenAI funded FrontierMath and held early access to the problem set and solutions, an arrangement Epoch AI had not disclosed to the contributors who had just handed over unpublished work. The disclosure surfaces only after outside pressure, not as a voluntary heads-up.

Days later: Andreas Thom posts on Mathstodon, asking the exact question those contributors were implicitly answering back in November, except now it's out loud and in public. The December disclosure did not settle whether the arrangement was a one-off mistake or a pattern, and Thom's post is the field saying so.

The through-line isn't that OpenAI lied about anything specific. It's that mathematicians keep discovering the terms of these arrangements after they've already contributed the work, never before. Every round of this story has the same shape: a lab gets access to unpublished material through a benchmark or research partnership, the funding relationship surfaces later than the contribution did, and the field has to decide, retroactively, whether that was acceptable. That's a structural problem, not a lapse, and it will keep recurring as long as benchmark funding and model training sit inside the same company deciding what counts as disclosure.

If you're a researcher weighing whether to share an unsolved problem with an AI lab this year, the practical answer is: ask who funds the benchmark before you ask how good the model is. The same skepticism belongs anywhere a lab grades its own homework — we've covered why AI models still stumble on math tests that look solved and how much a builder should actually trust an AI's own quality reports.

Expect at least one more math-benchmark funding arrangement to become public under pressure, rather than by voluntary disclosure, before the middle of 2025 — the incentive that produced FrontierMath's quiet arrangement hasn't gone anywhere.

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IEEE Spectrum's Self-Driving Safety Claim Isn't the Whole Story

Vin Patel — Thu, 10 Sep 2026 11:33:05 +0000

Originally published at vinpatel.com

IEEE Spectrum says the proof that self-driving cars save lives is growing. The proof itself is still mostly the industry grading its own homework.

The claim, as the industry and its boosters make it, is straightforward: autonomous vehicles crash less often than human drivers, hurt fewer people when they do crash, and the pattern strengthens as more of them hit public roads. That's the pitch investors hear, the pitch regulators hear, and the pitch riders hear before they get in.

What's measurable today is thinner than the claim suggests. Spectrum itself frames this as "growing proof," not settled proof — a deliberate hedge. Safety comparisons this early in a technology's deployment depend on which miles get counted, which crashes get reported, and who's doing the counting. None of that is standardized across the companies running these fleets, and the raw incident data an outside researcher would need to check the math isn't uniformly released.

The gap exists because the entities best positioned to prove the safety case are the same entities with the most to gain from the public believing it. That's not a motive problem, it's a measurement problem. There's no independent, mandatory reporting standard forcing every operator to disclose crashes, near-misses, and miles driven in the same format, audited the same way. Without that, "proof" is an aggregation of self-reported numbers and comparisons that don't always control for the fact that autonomous fleets currently avoid the hardest driving conditions — bad weather, unmapped roads, dense unpredictable traffic — that produce a disproportionate share of human crashes.

What would actually close the gap is boring and specific: a regulator-mandated, standardized incident reporting format applied to every operator equally, third-party audits of raw telemetry instead of company-published summaries, and safety comparisons that control for route difficulty rather than raw miles driven. Until that exists, "growing proof" is a real signal worth tracking, not a verdict worth repeating.

For builders shipping anything safety-adjacent, the same lesson shows up in agentic systems: a benchmark score from the team that built the product is not evidence, it's a claim waiting on an audit. The guardrail work in Forge is a useful reference for what independently verifiable improvement looks like versus a headline number.

Track this one before you cite it anywhere that matters. Field notes on stories like this land daily — subscribe at vinpatel.com/subscribe/ if you want the gap between claim and verified reality flagged before it spreads.

Everyone Read The Anthropic Resignation Tweet As Insider News. It Wasn't.

Vin Patel — Wed, 09 Sep 2026 07:11:30 +0000

Originally published at vinpatel.com

Everyone reading "I resigned from Anthropic today" as an insider leak is missing the real story: it is one unverified account, and Hacker News just amplified it into a headline it cannot back up.

The post, published under the handle hilbertspaess, surfaced on Hacker News under that exact title, and it climbed the rankings the way any first-person "I quit" post does: fast, with no editorial check on whether the account behind it ever worked at Anthropic at all.

That gap matters for anyone who treats Hacker News placement as due diligence. Recruiters scanning for attrition signals before extending an offer. Founders benchmarking a lab's internal health before a partnership call. Journalists building a trend piece out of a single anecdote. None of them get verification from a ranking. They get a vote count.

Here is the mechanism worth understanding: Hacker News ranks by upvotes and comment velocity, not by sourcing. A resignation post from an anonymous account competes on the same front page as an audited filing or a peer-reviewed paper, and the interface gives you no signal telling you which one has actually been checked. The board surfaces attention, not accuracy. That has been true of every dramatic "I quit [famous company]" post that has ever trended, and it will be true of the next one, regardless of which lab gets named in the title.

So the accurate read of this story is not "Anthropic has a morale problem." It is: an unverified account said something dramatic about Anthropic, and it worked, because dramatic and unverified travel at exactly the same speed on that board. If you are trying to track how frontier labs like Anthropic are actually shipping and staffing, treat single-poster claims as anecdote, not evidence, and wait for something with a name and a record attached to it.

For a closer look at how Anthropic's own output has actually held up under testing, the Claude Code quality reports piece and the Claude Opus 4.8 breakdown are both grounded in behavior you can reproduce, not a tweet. Get the next verified read before the noise does: subscribe at vinpatel.com/subscribe/.

Mistral Raises €3 Billion To Be Sovereign And Frontier At Once

Vin Patel — Tue, 08 Sep 2026 11:24:11 +0000

Originally published at vinpatel.com

Mistral raises €3B. The headline is not the story — the page slug is: mistral-makes-sovereign-open-weight-ai-to-frontier. Four words stacked together, and normally you only get to pick two.

The announcement wants you to read this as one story: Europe's answer to the big US labs just got funded. The artefact — the URL Mistral chose to put in front of every reader and every search engine — states something narrower and stranger. It claims three things at once: sovereign, meaning built and governed on infrastructure that isn't rented from a US hyperscaler; open-weight, meaning the model files are downloadable, not gated behind an API; and frontier, meaning competitive with the best closed models anywhere. Historically those three don't travel together. Sovereignty is a compute and governance story. Open weights are a distribution story that has mostly lived a tier or two below the frontier, because the labs that actually reach frontier capability keep the best checkpoint closed and monetize it through the API — a released weight file can't be metered.

€3B buys a lot of GPU hours. It doesn't, by itself, resolve the tension baked into that slug. If Mistral genuinely ships frontier capability as open weight, it's choosing to compete on infrastructure and enterprise contracts instead of on model access — betting that governments and companies building on sovereign soil will pay for control and compliance even when the weights are free to download. That's a real, buildable business. It's also a different business than the one every other frontier lab is running, which is why the raise reads differently depending on which of the three words you weight.

For builders, that's the part worth tracking, not the euro figure. If Mistral holds the open-weight line all the way to frontier, you get a self-hostable model at a capability tier that hasn't been available outside a closed API before. If "frontier" quietly becomes the closed tier while only the earlier checkpoints stay open, you get the same trade-off you already have with everyone else.

For what "frontier" actually means once you start benchmarking against it, Claude Opus 4.8 is the closer read. For what a raise this size restructures underneath a company once the check clears, the SpaceX IPO breakdown is the better analog than anything AI-specific.

The raise doesn't say which version of Mistral shows up. Neither does the slug. Field notes on which way it breaks land at vinpatel.com/subscribe/ before the pattern is obvious to everyone else.