Tech Friend AJ (@techfren) header
Tech Friend AJ (@techfren) profile picture

Follow for the sauce

San Francisco, CA
Joined June 2016
Photo by Tech Friend AJ (@techfren)Photo by Tech Friend AJ (@techfren)Photo by Tech Friend AJ (@techfren)Photo by Tech Friend AJ (@techfren)Photo by Tech Friend AJ (@techfren)Photo by Tech Friend AJ (@techfren)Photo by Tech Friend AJ (@techfren)Photo by Tech Friend AJ (@techfren)Photo by Tech Friend AJ (@techfren)Photo by Tech Friend AJ (@techfren)
Tech Friend AJ retweeted
We spent months on this so you don’t have to
Your RTX 3090 ships with about half its prefill speed locked away. @0xSero explains how, plus the 81GB model he runs on a base M4 Mac mini with a 3090 over Thunderbolt 5. We also cover his hacked X account, the OpenCode hack, and why chips ship locked. 0:00 Cold open 1:06 Local models are good enough 2:11 Skills that make local models repeatable 4:01 Having models write you a wiki 5:21 AJ's video editor and Opus 5.5 6:29 Growing on X 8:03 Meta's Project Lazarus 9:21 Why X is different 10:30 Binance hack and digital cleanup 12:11 AI slop vs human slop 13:07 Faceless 3D videos vs human content 15:40 Social media funnels 16:47 Why Sero doesn't make short-form 17:31 AI feeds and three-minute learning 20:00 Management work, games and reading 21:45 BB and next-step buttons 22:44 Claude artifacts as a brand system 24:19 Where Opus still falls short 26:02 Claude watermarking its text 27:53 Why Binance prices barely moved 28:59 What to do with $6B 29:21 Living in San Francisco 30:15 Teaching people to use agents 34:17 Getting into public speaking 35:55 DHH's OS built for agents 37:01 How Sero's X account got hacked 38:55 An agent exposed Sero's file system 39:35 How OpenCode got hacked 41:08 What Binance does next 41:37 Anthropic, xAI and Colossus 43:17 Data centers offshore and in space 44:02 Vera Rubin and ASIC chips 45:57 Etched and disaggregated inference 48:53 NVMe offloading and GPU acceleration 51:36 NVMe as cheap RAM 55:07 eGPU: Mac mini + RTX 3090 57:38 Recipes: models as game cartridges 1:00:18 The hidden RTX 3090 speed 1:02:18 Why hardware ships locked 1:03:18 NVLink and multi-GPU interconnect 1:05:58 How chips are manufactured 1:07:54 Adding VRAM to 4090s and 5090s 1:11:09 Have a model crack your GPU 1:11:56 DeepSeek and GLM for reverse engineering
3
3
1
133
7,096
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.
56
25
19
259
22,618
Tech Friend AJ retweeted
Live at Best Buy in SF w/ the Clawfather .@steipete & .@techfren
12
7
1
115
4,469
Tech Friend AJ retweeted
honestly, most code before ai was also slop
616
881
261
16,830
443,679
Tech Friend AJ retweeted
Updated Dotmax , 100% free terminal graphics, *rusty no strings, or follow up email newsletters! ever! - dotmax-sable.vercel.app/
2
10
717
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!
9
11
7
75
15,387
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.gq/NathanWilbanks_/status…
I hooked Doom to a basic 187M param zero shot sequence classification model to do similar 10x+ faster by classifying in a single forward pass plays doom, but also works w other biz tasks too: - intent detection - prioritizing tasks - customer support routing - scoring - etc
2
9
2
112
19,751
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.gq/NathanWilbanks_/status…
4
2
1
25
9,279
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
3
1
5
501
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.
#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…
4
1
1
11
1,249
Tech Friend AJ retweeted
Origin Origin Origin! Let’s go deep on my livestream at 11am PST.
Origin, our code hosting platform, is now live. It's fast, easy to use, and deeply integrated with Cursor. Get started by syncing your repos from GitHub.
6
2
51
4,650
Tech Friend AJ retweeted
I’ll giveaway my Mac Mini on stream if we hit top 10. Lock in: local.ai/rayfernando1337/inv…
10
6
2
146
23,229
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.
8
4
49
1,688
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
62
14
1
352
36,016
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
8
12
3
205
18,152
Tech Friend AJ retweeted
loops and graphs are productivity theatre just prompt your agent bro
52
18
9
306
15,531
Tech Friend AJ retweeted
Been been
1
1
203
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
2
1
7
542