abdullin

7 ms·

1.
by abdullin 2mo ago
I’m struggling myself a bit with the components and routers in HTMX/go. Can you recommend any source code or reading on that topic?
2.
by abdullin 3mo ago
#1 - the tricky part there is in scenarios from a few AI Native teams. There often are a multiple agents rolling out linked changesets to a bunch of documents on behalf of controlling humans. Eg updating compliance policy, and references an
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by abdullin 3mo ago
Nice approach. Personally I’ve been trying very hard to migrate away from git+Obsidian project setup according to the OpenAI Harness Engineering. It works wonderfully in Codex Desktop. The only gotcha - I want to share knowledge bases with
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by abdullin 4mo ago
Working on benchmark arena for AI agents with my wife. We grab interesting business problems, turn them into fun challenges for hundreds of AI engineers to find the best architecture for. Insights are shared back with the community. It is a
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by abdullin 5mo ago
I reproduced this on my account. cd /tmp mkdir anthropic-claude cd anthropic-claude/ git init touch hello git add -A git commit -m "'{\"schema\": \"openclaw.inbound_meta.v1\&q
6.
by abdullin 5mo ago
I built a platform to learn how to build personal AI agents and test them with fast feedback. It is free for individuals and small teams. Platform deterministically generates tasks, creates environments for them, observes AI agents and then
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by abdullin 6mo ago
I liked NixOS pre-LLM era, since it allowed me to manage a couple of servers in a reproducible way. Ability to reboot back to a stable configuration felt like magic. Nowadays I love it, since I can let Codex manage the servers for me. “Here
8.
by abdullin 10mo ago
Yep, exactly the same concept. Except not live-streaming, but giving out a lot of multi-step tasks that require reasoning and adaptation. Here is a screenshot of a test task: https://www.linkedin.com/posts/abdullin_ddd-
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by abdullin 10mo ago
I’m working on a platform to run a friendly competition in “who builds the best reasoning AI Agent”. Each participating team (got 300 signups so far) will get a set of text tasks and a set of simulated APIs to solve them. For instance the t
10.
by abdullin 1y ago
> Inference is (mostly) stateless Quite the opposite. Context caching requires state (K/V cache) close to the VRAM. Streaming requires state. Constrained decoding (known as Structured Outputs) also requires state.
11.
by abdullin 1y ago
Is it similar to what OpenAI Codex does with isolated environments per agent run?
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by abdullin 1y ago
In systems like that you can record human interactions with the old version, replay against the new one and compare outcomes. Is there a delta? Debug and add a unit test to capture the bug. Then fix and move to the next delta.
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by abdullin 1y ago
I grew to like migration projects like that. Currently working on migration of 30yo ERP without tests in Progress to Kotlin+PostgreSQL. AI agents don’t care which code to read or convert into tests. They just need an automated feedback loo
14.
Tracking PR volume from AI coding agents(prarena.ai)
1 points by abdullin 1y ago|0 comments
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by abdullin 1y ago
I think there are two different layers that get frequently mixed. (1) LLMs as models - just the weights and an inference engine. These are just tools like hammers. There is a wide variety of models, starting from transparent and useless IBM
16.
by abdullin 1y ago
Yes. I believe, the experience will get better. Plus more AI vendors will catch up with OpenAI and offer similar experiences in their products. It will just take a few months.
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by abdullin 1y ago
Here is another way to look at the problem. There is a team of 5 people that are passionate about their indigenous language and want to preserve it from disappearing. They are using AI+Coding tools to: (1) Process and prepare a ton of vario
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by abdullin 1y ago
It is actually funny that current AI+Coding tools benefit a lot from domain context and other information along the lines of Domain-Driven Design (which was inspired by the pattern language of C. Alexander). A few teams have started incorpo
19.
by abdullin 1y ago
Agreed. AI is just a tool. Letting in run the show is essentially what the vibe-coding is. It is a fun activity for prototyping, but tends to accumulate problems and tech debt at an astonishing pace. Code, manually crafted by professionals,
20.
by abdullin 1y ago
Exactly! This is why there has to be "write me a detailed implementation plan" step in between. Which files is it going to change, how, what are the gotchas, which tests will be affected or added etc. It is easier to review one do
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by abdullin 1y ago
I guess, it depends on the case and the approach. It works really nice with the following approach (distilled from experiences reported by multiple companies) (1) Augment codebase with explanatory texts that describe individual modules, int
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by abdullin 1y ago
Running tests is already an engineering problem. In one of the systems (supply chain SaaS) we invested so much effort in having good tests in a simulated environment, that we could run full-stack tests at kHz. Roughly ~5k tests per second o
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by abdullin 1y ago
Claude's approach is currently a bit dated. Cursor.sh agents or especially OpenAI Codex illustrate that a tool doesn't need to keep on stuffing context window with irrelevant information in order to make progress on a task. And if
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by abdullin 1y ago
A simple rule applies: "No matter what tool created the code, you are still responsible for what you merge into main". As such, task of verification, still falls on hands of engineers. Given that and proper processes, modern tooli
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by abdullin 1y ago
Humans tend to lack inhumane patience.
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by abdullin 1y ago
This is the workflow that ChatGPT Codex demonstrates nicely. Launch any number of «robotic» tasks in parallel, then go on your own. Come back later to review the results and pick good ones.
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by abdullin 1y ago
Tight feedback loops are the key in working productively with software. I see that in codebases up to 700k lines of code (legacy 30yo 4GL ERP systems). The best part is that AI-driven systems are fine with running even more tight loops than
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by abdullin 1y ago
Even more than that. With Structured Outputs we essentially control layout of the response, so we can force LLM to go through different parts of the completion in a predefined order. One way teams exploit that - force LLM to go through a pr
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by abdullin 1y ago
I'd say that the core principles stayed the same for more than a year by now. What is changing - constraints are relaxing, making things easier than they were before. E.g. where you needed a complex RAG to accomplish some task, now Gem
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by abdullin 1y ago
My current workflow with Codex is (coding environment from OpenAI): (1) Ask to write an implementation plan for a specific change or a feature. It will go through the source code, look up references, make notes and produce a plan (2) Review

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