Robert Balicki (👀 @IsographLabs)

16.7K posts

Robert Balicki (👀 @IsographLabs)

Robert Balicki (👀 @IsographLabs)

@StatisticsFTW

@isographlabs framework author. Currently @Pinterest. Ex-React Data Team @Facebook. Co-organizer of #RustNYC. I like Rust, Relay, stats, GraphQL, React, JS

Manhattan, NY Katılım Nisan 2012
1.6K Takip Edilen1.7K Takipçiler
Robert Balicki (👀 @IsographLabs)
That's the thing: we can't just wait six months until you can just tell a model, "Make me a billion-dollar company, make no mistakes." Even in that case, the floor will have been raised, and those that more effectively use models (for example, giving them smaller tasks and not relying on them to do things that they're not very good at, like going through structured workflows) will still be outcompeting those that are trying to use the model for everything. I shipped 300 LLM-generated PRs in the last week. This wouldn't have been possible without "harness engineering", which is to say without giving the LLM a lot of structure around this. And in six months, I'll be again shipping more than your average bear again by providing structure and allowing LLMs to focus on the things that they're good at But you're 100% right that we can't get emotionally caught up in the specifics of how we're doing this.
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Pinboard
Pinboard@Pinboard·
The correct goddamn plural is platypodes. Defund NASA now
NASA's Johnson Space Center@NASA_Johnson

.@NASA’s 2025 Astronaut Candidate class now has a name. Meet the Platypi! ⭐ Ten explorers are currently training at NASA Johnson in Houston, mastering the technical and operational skills needed to become flight-eligible astronauts. Their missions? The International @Space_Station, the Moon, and eventually Mars. Learn more about the group: nasa.gov/centers-and-fa…

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Erik Bernhardsson
Erik Bernhardsson@bernhardsson·
In hindsight all the sandbox stuff is so obvious because containers without AI are basically just contners which makes no sense.
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Robert Balicki (👀 @IsographLabs)
You may be interested in Barnum, which is essentially a workflow engine that can send tasks to agents. This solves the context bloat problem and saves on tokens, and also lets you build something much more reliable than if you were having an agent be the orchestrator directly. x.com/StatisticsFTW/…
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Zbigniew Lukasiak
The limited resource in a machine build with llms and symbolic processors is the prompt lenght (i.e. context). There is the trivial context length - but there is also the complexity of it that counts: arxiv.org/html/2602.0107… , arxiv.org/pdf/2509.21361 When building agentic sytems we should start the design from this.
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Kyle Mistele 🏴‍☠️
Kyle Mistele 🏴‍☠️@0xblacklight·
have been thinking a lot about how tools/subagents/skills/CLIs/code mode all fit together and how we make coding agents better without 10x-ing token spend I think it boils down to something like tool modules - MCPs / CLIs / tool search / code mode tools. logical groups or bundles of tools. instruction modules - i like this one from @dexhorthy - think a custom prompt or set of instructions, claude slash-command style context windows - sub-agents, compaction, context pruning etc
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Robert Balicki (👀 @IsographLabs)
Something you're missing is having the agent do less work. So, for example, instead of having the agent run ESLint, you instead have a function that runs ESLint and only invokes an agent to fix the issues if issues are actually found. That's the premise of Barnum. x.com/StatisticsFTW/… This also makes it extremely easy to prevent context bloat.
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Robert Balicki (👀 @IsographLabs)
There are folks doing more product-related refactors using the same techniques, but I focused on non-controversial code improvements. For example: - Removing dead code - Avoiding unnecessary clones - Extracting constants - Making impossible states unrepresentable - Extracting functions and sub-components - Removing unused props and type parameters - Narrowing input and output types as much as possible, etc.
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Feifan Zhou
Feifan Zhou@FeifanZ·
I once talked to someone with a 12-step workflow that involved multiple rounds of planning, few of which involved LLMs actually touching the code, and they were convinced it led to better results.
Quinn Slack@sqs

An uncomfortable truth about building agents/models: By default, your most lucrative, most-smitten customers will be those using intricate out-of-band techniques that are exorbitantly expensive and probably net negative (but that they love). It's a very weird incentive. You can't and don't want to indulge this. There's nothing wrong with experimentation, but if you saw what every agent company sees, you'd know this goes way beyond experimentation. Amp tries really hard to prevent this: limiting long context, showing prices, not recommending swarms or loops prematurely, strongly advising against big MCPs, killing features that have high usage but that aren't worth it anymore, and just generally staying away from any hype train we don't have a good gut feeling about. Pi and OpenCode are also particularly good and outspoken here. But if you have growth targets to hit, investors to pitch, and salespeople to keep happy, or if you didn't start this way from day 1, I can see it being tricky. At Amp, we're profitable, don't have salespeople, and have no sales/growth targets to hit, so we have it relatively easy. I often wonder what this tension is like inside other companies building agents. (And for the record: if you've shown me your Amp workflow and I haven't told you this directly, this post is not about you. :)

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Robert Balicki (👀 @IsographLabs)
@FeifanZ Yeah, one hundred percent. There's a separate workflow for each job I'm trying to do. It just happens that one of the jobs is "ship an absurd number of refactors without breaking production."
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Feifan Zhou
Feifan Zhou@FeifanZ·
@StatisticsFTW oh yes, i’m generally in favor of breaking up problems! but a single dogmatic workflow for everything is probably not the right solution
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Thorsten Ball
Thorsten Ball@thorstenball·
Lately, whenever I open this app and see the latest tricks, and hacks, and notes, and workflows, and spec here and skill there, I can't help but think: All of this will be washed away by the models. Every Markdown file that's precious to you right now will be gone.
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Zack Korman
Zack Korman@ZackKorman·
You can hide these !commands in html comments so people don't see them when reading the skill. The command executes without the AI even knowing about it.
Zack Korman tweet media
Lydia Hallie ✨@lydiahallie

if your skill depends on dynamic content, you can embed !`command` in your SKILL.md to inject shell output directly into the prompt Claude Code runs it when the skill is invoked and swaps the placeholder inline, the model only sees the result!

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Robert Balicki (👀 @IsographLabs)
@nayshins I have been running an orchestration of agents that target 11 or so refactors (eg dead code removal, making impossible state unrepresentable, etc) and doing my best to expunge bad practices, using barnum-circus.github.io So far, shipped about 300 PRs in the past week
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Jake
Jake@nayshins·
Has anyone documented all the code slop patterns yet? I want to lint for them and banish them to hades.
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Nic Barker
Nic Barker@nicbarkeragain·
A classic example: - Click a button to open a dropdown - Open dropdown should close if you click outside - Wait 1 frame after the button click to display dropdown: it works perfectly - Open dropdown same frame: it will dismiss itself instantly (click is still active & "outside")
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Rijndael
Rijndael@rot13maxi·
Sometimes my agent makes mistakes. It clobbers a feature when merging a PR. It tells me something is done and passes the tests but the basic functionality is missing. This is usually a good opportunity to add some additional tooling or guardrails to your workflow. 🧵
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Rijndael
Rijndael@rot13maxi·
Here's the skill: github.com/rot13maxi/agen… next time your agent makes a mistake and messes up your project, just say /coe or /post-mortem intake form broke after merging 3 PRs
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