KlarkC

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@joaompfe devenv mentioned 🔥

Imagine starting a new project and not having a flake.nix (or a devenv.nix).

labs optimizing for totally wrong things imo and that kinda makes me think back on what lecun says all the time about world models. i like the idea of a duplex voice model, but it’s trained for random pleasantries as Kevin says. the “aha” and “hmm”s are not great, they are forced. models do not understand the nature of a duplex communication, reward hacking towards what’s expected. which is weird. same with llms/coding models. latest models are forced to take actions, maybe artifacts of benchmaxxing. they seek rewards on implementation and rushing towards building things before understanding why and how’s.

Wait, what? A 35B MoE beats Kimi-K2.6 and DeepSeek-V4-pro on long-horizon agents. Agents-A1 👀 I need to read this! arXiv 2606.30616 🤯 arxiv.org/pdf/2606.30616

@sridca 🔥

You can now 'sleep' terminals in Kolu. And 'wake' them up anytime. This is useful to suspend sessions that you won't need for the next few days (e.g.: when waiting on contributor's response). This saves on CPU/memory usage.

so cool! herdr is becoming an agent runtime. manage your agents from anywhere: your terminal, your vps, your phone; or apparently, your desk 👀

@TheAhmadOsman And, here I am, looking for a M4 to run local AI 💀

I have a 48GB M4 Pro for Local AI Great device to SSH into my GPUs nodes

local first wins

My heuristic is that any diff an agent generates over ~1500 lines is too big and is indicative that the problem needs to be decomposed. This is my general pattern now for feature work: 1. Try to implement the whole feature, loosely guided. I call this the "draw the owl" prompt in reference to the meme. Expect garbage, you're going to get garbage. 2. If the diff is less than 1500 lines, review it and iterate normally. If the diff is more than 1500 lines, prompt the agent to decompose the problem into atomic, incremental, reviewable tasks. Simultaneously, do this yourself. 3. Agents will very often make these tasks way too specific to the shape they solved. You need to massage it into the right general shape. Do that. 4. Kick off new agents to work on those incremental things (as parallelized as possible). Apply the same rules. 5. At a certain, point, repeat the "draw the owl" prompt. At some point, you will get beneath your review-ability threshold. This has been producing consistently high quality, maintainable, reviewable chunks of code that have a good handoff to either merge as-is or human refinement. And with the latest frontier models at xhigh thinking, these are all slow enough that you can usually have multiple going concurrently while you are actively reviewing others or working on your own tasks. HITL (human-in-the-loop) agents are still super important, especially for feature work. Features touch the human boundary in terms of UI, API, etc. And net new stuff can introduce pathologies in the architecture that violate desired invariants (these should be represented in specs or tests but we aren't perfect!). I know a lot of the leading edge agentic discourse is about "loops" and agents driving agents continuously. I do some of that (will report on that later). But, in terms of raw daily get-shit-done type of work, this is my most rewarding pattern at the moment.

moshi + herdr shortcuts makes navigating multiple panes on mobile so easy i love it

Life after Fable 5 is not the same anymore... > i write detailed specs for opus 4.8 > opus takes 15 minutes to execute > misses 3/5 things > over engineers 4 new things > me: why this? why that? > opus: "You're absolutely right......." > repeat 90 times Results: 35% of weekly quota gone, task still undone!

@VictorTaelin @cjzafir We should be talking more about useless intelligence

@cjzafir exactly this and I was praising this model a week ago I don't get it

@Tono_Ken3 @cyburke Looking for AWQ/GPT/AutoRound 4bit versions 🙃

@cyburke Here you are :D huggingface.co/sakamakismile/…

軽量級の注目株Gemma-4-12BにComposer2.5のエッセンスを加えて、間違えたところをFable5にやり直しさせましたとモデルカードは言っています このモデルを試さないのは罪ですｗ huggingface.co/yuxinlu1/gemma…

@0xSero What if we had a destilled version of this 28B version on fable 5 + composer 2.5 🥹 the closest we have right now is this 14B version: huggingface.co/tvall43/Qwen3.…

Thank you Benjamin. I am very happy you shared this, i noticed the same thing when benchmarking DSV4-Flash and started tracking this in my benchmarking

herdr 0.7.0 is out, and it's a major one: it introduces plugins! the idea is simple: herdr stays lean, and everything custom gets extended through plugins. shareable, scoped, built however you want, to fit your own flow. with this release we're also shipping a few examples of what the plugin system can do. first up: a telegram plugin. herdr already controls your agents and knows their status, so the plugin just hooks into agent events and pings telegram the moment one needs you. notification lands → `herdr --remote` or ssh from your phone → straight back to the agent that needs you.

@sridca Looking forward to it. Also, if I may, take a look at Herdr. They've been doing a lot of things, and maybe you can get some inspiration from them. One thing I really wish for is the ability to cycle between blocked agents. I'm even working on a patch for it.

Instead of baking tmux'ish stuff into Kolu, I'll decouple (much like drishti, odu) that as a better tmux/zmx and then use that as Kolu's pty core. Prepare to be dazzled. 🧙

I don't understand the obsession of the big labs with making the models bigger and fancier. Well-targeted performance of AI is already superhuman. Just advance context management in tooling, that has way more upside than adding another B to the model size.

@malikwas1f @0xSero @sakurayukiai Solid call on AutoRound! Intel/Qwen3.6-35B-A3B-int4-mixed-AutoRound keeps shared experts in higher precision. Much better than AWQ for MoE routing/intelligence.

@0xSero @sakurayukiai why not intel autoround?

AWQ Qwen-3.6-35B doing much worse for some reason. You can see how compression increasingly decreases intelligence.

Programming is understanding. If you don't understand what you are doing, you are not programming. You are generating text.

🤣

@ryanlpeterman 34:00 The best explanation of the value of lazy evaluation that I've ever seen.

Simon Peyton Jones is the co-creator of Haskell (pure functional programming language) and I interviewed him about functional programming, why it matters, and his thoughts on other programming languages. In this episode: • Useful and useless programming languages • Rust vs C • Haskell vs OCaml • Why functional programming matters • Static languages and their value for LLMs • Why Excel is his 2nd favorite programming language Where to watch: • YouTube - youtu.be/xcB_LF3cdqw • Spotify - open.spotify.com/episode/5d9VR5… • Apple Podcasts - podcasts.apple.com/us/podcast/the… • Transcript - developing.dev/p/co-creator-o… Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at workos.com Chapters: 00:00 - Intro 00:39 - What functional programming is 09:18 - Downsides of functional programming 10:53 - Specialized hardware for functional programming 21:47 - Haskell is useless 25:59 - Rust vs C 28:26 - Haskell vs OCaml 35:26 - Side effects in Haskell 44:26 - Type systems 57:30 - How the Haskell compiler works 01:04:35 - Why Haskell is talked about more than used 01:09:07 - Avoiding success at all costs 01:11:12 - LLMs and programming languages 01:13:57 - New programming language design 01:15:59 - Should students continue to learn programming 01:22:33 - Why Excel is is 2nd favorite programming language 01:25:04 - Advice for his younger self