Eeshan

Apple talking more local AI at WWDC 26 is a great thing, and positive for open source as well. The learning curve for the average person is still quite steep, but it was great to see my favs Pi (@badlogicgames), OpenCode (@thdxr), @lmstudio showing up on screen! oMLX (@jundotkim) should have been there too, making MLX more accessible to users. Congrats to all you open source heroes! Wrote more about it here: theasymptotic.substack.com/p/local-ai-ser…

@ViberPsychosis @davideciffa Gemma 4 12B or huggingface.co/Jackrong/Qwopu… are very capable on a 24gb mac mini.

@davideciffa Got a 24gb Mac. What’s good for Hermes?

Go local. You only need a 16GB card to get started with decent models!

@readswithravi Harry Potter and the Sorcerer's stone. Need an escape along with some nostalgia.

What are you all currently reading?

@GrizzledTexan @haridigresses For sure. They'll never match the speed & quality of frontier AI, but this is where we can make a tradeoff b/w convenience and cost (and privacy). Small models are good enough for skilled people who want to use it for narrow tasks, and not for multi-agent loops.

@notesundrground @haridigresses It will be really slow even on the best of laptops.

@haridigresses @GrizzledTexan I'm just going to wait it out until open source models are good enough to run on laptops. Not spending money on GPUs either. Qwen 3.6 and Gemma 4 series are already almost there. God bless open source!

@GrizzledTexan Had dinner with a friend last night who's done the exact same thing. Bought a GPU and is running a setup at home.

@codevsdev they didn't .. coding was invented by LLMs in 2023 right?

how did people even learn to code when there was no docs, no YouTube... nothing?

@ThePrimeagen I built something like this for myself 6 months ago, which doesn't require you to upload your code to a 3rd party site. Everything is analyzed locally and I'm not selling anything. Just cool view of my coding activity + persona + wrapped style page howiprompt.eeshans.com

Is this this taste thing I keep hearing about?

@atmoio Just like all of the LLM outputs, it's writing is a crude "approximation" of human writing.

The funny thing about the AI “intelligence” revolution is that AI is basically a writer. Writing what? Anything. Prose. Poetry. Code. Math. It can write. And the question is, if you automated writing, how disruptive is that? It’s not nothing. But also, it’s a pretty whimsical premise right? Like ha wow writing is solved, THEREFORE EVERYONE AND EVERYTHING IS TRULY FUCKED. Like what? How did you get there? IT CAN WRITE A LOT. REALLY FAST. oooo, spooky Clearly the non-delusional take is every human gains an expensive writing/research assistant. And this is clearly, demonstrably the full extent of the revolution.

@orcdev Spotify wrapped style analytics dashboard to analyze your AI conversations for activity, politeness, vibe-coder index etc. Source: github.com/eeshansrivasta… Demo: howiprompt.eeshans.com

Give me your open source projects ⚔️ I'm collecting projects for a new YouTube series. Drop a link below and tell me what you're building 👇

@hthieblot Stumbleupon

Anyone who surfed the early web between 1995-2010. What’s the one website/app you still think about?

@helloiamleonie This is with Hermes btw.

@helloiamleonie Managing some Obsidian workflows. Daily gratitude & wins log, extracting the wins from my vault activity.

just curious: what’s the most useful thing your OpenClaw, Hermes Agent, etc. is doing for you?

One of the best @MKBHD videos! Truly fascinating to learn about all this NBA tech and the super talented people behind it.

Of course credit to pi.dev @badlogicgames for the minimal harness that works great with local models. My workflow is also just 3 skills and no subagents to fit my uni-tasking mindset.

New post in my local AI series where I continue testing and evaluating local LLMs that don't need any data centers to run. This time I gave four local models a live A/B test to analyze, by making API calls to a real database, pull experiment data, run the correct statistical tests, and make a product decision. Caveat: I don't really NEED an LLM to automate experiment analysis, nor do I think it's a good real-world LLM use case, but this was a very interesting test of complex multi-step tool calling and hallucination resistance over a long procedural task. In short, these tiny <35B parameter models are capable enough for such narrow agentic tasks. Qwen 3.6 35B A3B is still my M4 Mac Pro laptop champion over multiple benchmarks. @Alibaba_Qwen I used my live A/B test memory game as the source, and wrote about the methodology and results on my substack: theasymptotic.substack.com/p/local-ai-ser… Live A/B test at absim.eeshans.com Workbench + gallery at localai.eeshans.com Read if you're interested in this domain as well, and would love to hear your thoughts.

@badlogicgames Same. The ones that don't need massive data centers to run.

except for open weights models i can run locally :D

when i worked on game dev tools (including mobile) i was always excited when a new WWDC or Google I/O dropped. Usually meant new stuff. when i started working with LLMs sometime in 2023, i was always excited about new model drops. I do not have excitement anymore.

@F2aldi My daily driver. And I use it minimally as well. Only added a couple of extensions for web search and that’s it. No subagents or plan mode. Fits my unitasking workflow perfectly.

Has anyone tried Pi harness seriously? I’m curious how it compares to OpenCode for open-weight model workflows. On paper Pi feels more minimal/customizable, while OpenCode feels more ready out of the box. Which one feels better in real coding work?

@buildwithsid Super clean! Loved the reddit roast app :D

updated my portfolio website my cleanest design yet 👀

@levie Just realized that you're a CEO too .. it's refreshing to see this level of self-awareness and critical thinking. Thank you!

CEOs are uniquely prone to AI psychosis because they’re sufficiently distant from the last mile of work that still has to happen to generate most value with AI. So when they play with AI, they see the happy path results, often not considering the next 10 or 20 things that have to happen to get sustainable results from agents. “Look I made this awesome product prototype”. Yes but you didn’t have to review the code before it went into production and fix a bunch of issues. “Look I generated a contract”. Yes but you didn’t verify all the terms before it goes out to the counterparty and didn’t have to wire up all the past contracts to work with. The best thing you can do as a CEO is to use AI a *ton* to figure out the real implications of agents in the enterprise, and come out the other side with an appreciation for both the upside and the real work that goes into them.