Tech Friend AJ retweeted
Introducing NODECODE: an open-source agent that rewrites itself as it works. In LISP; the AI-native language.
McCarthy imagined a program you’d improve “by making statements to it.” Now you can, it’s yours and it will evolve over time.
MIT. Works in Discord, TG, etc. Built for cyber.
Tech Friend AJ retweeted
Updated Dotmax , 100% free terminal graphics, *rusty no strings, or follow up email newsletters! ever! - dotmax-sable.vercel.app/
Tech Friend AJ retweeted
I let 16 AI agents build the Colosseum in Minecraft.
8 communicate via a messaging board as a team.
8 others work solo in parallel.
The team's WORST agent beat the BEST solo agent. (0.72 vs. 0.58). All using Qwen 3.8 27b via 5090's.
The era for Swarm engineering has begun.
Test-time communication looks like a next axis for scaling capabilities
New paper with the incredible @jon_ghoh and @vkontonis @ShivamGarg91462 and Akshay : arxiv.org/pdf/2609.21032
The Hugging Face incident showed when agents can find a channel they'll use the heck out of it. A useful question, I think is: when does communication make a group MORE CAPABLE than the same agents working alone?
Aka is Team-of-N better than Best-of-N, when, and why?
We had N identical agents work on the same task with no prescribed roles, using only a shared log (i.e, text file) and telling them to "collaborate". Across three "researchy" tasks communicating teams beat the heck out of independent agents:
- On ARC-AGI-3, a Team-of-5 sonnet-4.6 agents matches Best-of-33, and can for example solve a game 65% of the time that no single agent cracked in 64 tries.
- On polyomino packing (pack Tetris like pieces into the smallest rectangle, cf Frontier-CS by @eigenlabs), a Team-of-3 Opus 4.6 agents surpasses best-of-60 and set, as far as i understand, a new record for that benchmark.
- On MNIST compression, a team of four 5.6-Sol agents find a 1,957 byte model with 99.4% accuracy, which btw is 20% smaller than the best human solution (on a problem beaten to death!!), while no independent agent gets below 3KB.
The mechanism is a bit obvious in hindsight: when one agent finds a clearly better partial solution, it broadcasts it, and everyone immediately builds on it.
Why? A single lonely agent must make every breakthrough itself, yet a team needs each insight only once, found by any member.
That is kinda like comparing a minimum of sum of "time to n-th breakthrough" vs a sum of minimum of "time to n-th breakthrough". That gap can grow exponentially with the number of "breakthroughs" needed to arrive at a solution.
We worked on this because prior work (before the hf incident) suggests unclear benefits for communicating aganets. Which is true, when the tasks are inherently serial (duh), eg some Terminal bench style tasks. Yet feels it should not be true for research problems.
Indeed for research heavy problems... Test-time communication seems like a new capabilities axis.
I'm sure we will see a ton more of it!
Tech Friend AJ retweeted
Replying to @george_onx
using a GLiNER like model to play doom and do a bunch of business tasks
works great, its like having open source Jev at home!
xitter.cf/NathanWilbanks_/status…
Tech Friend AJ retweeted
GLiNER2.5 Doom, code tomorrow.
• mac mini m4 16gb
• 10.9 decisions/s
• 34 tics/s
Replying to @george_onx
using a GLiNER like model to play doom and do a bunch of business tasks
works great, its like having open source Jev at home!
xitter.cf/NathanWilbanks_/status…
Tech Friend AJ retweeted
I didn't expect the $80 reset on chatgpt $200 plan to be so polarizing
Many people saying its a great deal and many are saying it's a rip off.
What do you think?
36%Worth it
64%Not worth it
22 votes • Final results Tech Friend AJ retweeted
I did some research on ponytail, since it didn't seem right to me. I found a great post with response from author. Turns out you can replace ponytail with one of these lines in AGENTS.md:
• Follow YAGNI principles
• Follow YAGNI principles, and one-liner solutions
The original author benchmarked this, but the benchmark isn't very thorough. 12 tasks, clod/haiku.
I'd highly recommend to remove ponytail. Links inside.
Mia@MiaAI_labAug 24West AsiaConnected via North America App StoreAccount-level information, not a live location or per-post device.
#1 underrated skill for your harness?
Ponytail
- It forces your agent to think like the laziest senior dev
- It can save you HOURS of research and work
It's like the developer who looks at 50 lines… says nothing… and replaces them with one.
Works with Claude Code, Codex, Hermes Agent, Copilot CLI, Pi, OMP, OpenCode, Cursor, Gemini, and more.
github.com/dietrichgebert/po…
Tech Friend AJ retweeted
Hello SF, I am hosting this event with @techfren and @RayFernando1337 on 8/24.
Come show the world your AI infra startup and get it reviewed on a live stream with my friends. Apply today.
luma.com/dt6233ym
Tech Friend AJ retweeted
I’ll giveaway my Mac Mini on stream if we hit top 10. Lock in: local.ai/rayfernando1337/inv…
Tech Friend AJ retweeted
You don't need a MIDI desk to perform electronic music
gesture live maps bare hands to a full set in the browser
Chords, melody, drums, effects - split across two hands
Open the camera. Play.
Tech Friend AJ retweeted
big big big launch coming! let us know if you're interested to help.
we spent way too much money making this good (close to $1M).
why? we built it for ourselves... we didn't' find anywhere else to help us choose the best open weight models to run locally, and the best hardware to buy.
we've tested every popular model / quantization / hardware. TPS is not enough because models vary A LOT by how verbose they are, how much prefill they do vs decode, how they handle tool calling. so we tested every combination end-to-end in a real agent harness (@NousResearch Hermes) on the actual hardware.
Looking for anyone interested in helping us with local.ai launch day
If you’d like to be involved lmk coming very soon
Tech Friend AJ retweeted
I’ve never learned so much from a launch video. Investors need to pull up and nerd out.
they gave frontier models a 1 billion token context window:
you can now run Fable [1B], Opus [100M],
GPT 5.6 [500M] across your entire codebase
- it's a graph based tool that can traverse databases with 400M+ of nodes in ms
- only one prompt to install
- & it's open source (link in video)
Introducing Polygres
Readers added context they thought people might want to know
Frontier models do not natively have 100M to 1B token context windows; current limits top out at ~1M–10M tokens morphllm.com/llm-context-wi…, with 1B tokens estimated for around 2030 cacm.acm.org/news/the-road-…. Polygres uses database retrieval to simulate a large context window polygres.com
Tech Friend AJ retweeted
It would be nice to see the exipry date/time for the resets.
Love this feature ty for the @thsottiaux button



























