Roberto H Luna

@danielperk64718 Nah this shits noise. If they were applying a proper organizational structure I’d be applauding but they are not, and they are following last years standard of agent teams, with a CEO and all that shit

Paperclip got the crazy tech 💦💦💦💦

@cryptic_mazey Nah this shit doesn’t even properly use organization theory, it’s like the kid version of a company, just call it agent teams at this point

He might actually be the goat…

@hark_labs What the fuck kind of noise is this???

Introducing Hark, an AI lab building the most advanced, personal intelligence in the world. We're creating intelligent foundation models paired with next generation hardware designed to serve as a universal interface between humans and machines. hark.com

@CeciStalls 🧢

We're building the world's safest vibe coding platform.

@mil0theminer LOL

“Introducing [crappy name] The worlds FIRST agentic app builder”

@adisingh orgo.ai how is this different than this

Software for agents isn’t enough. We need computers for agents

@4shadowed @somewheresy Looks like a classic case of semantics to me. Call it what you want, Scam Altman posting about Peter joining OpenAI is all I need to know, to know it’s compromised

@robertohluna @somewheresy No it’s not. It’s owned by the OpenClaw Foundation, OpenAI is just one of our several sponsors

hermes agent will eat openclaw because openclaw is terrible at community management

@tomosman Yes there is no choice

Everyone is building the same thing.

@codyschneiderxx Done

I want you to build software I can operate with Claude Code everything everything can be operated with an agent

@advaitpaliwal orgo.ai @nickvasiles

Introducing Agent Computer Cloud computers for AI agents in <0.5s with persistent disk, shared credentials, and SSH access agentcomputer.ai

@jasonkneen @yiliush I find all your work quite interesting, you seem to have good ideas

@yiliush My own approach

source code now available github.com/collaborator-a…

@jennyzhangzt All this noise, yet still you haven’t figured out how to optimally encode the intent to the agents to have them know how and why to improve themselves

Introducing Hyperagents: an AI system that not only improves at solving tasks, but also improves how it improves itself. The Darwin Gödel Machine (DGM) demonstrated that open-ended self-improvement is possible by iteratively generating and evaluating improved agents, yet it relies on a key assumption: that improvements in task performance (e.g., coding ability) translate into improvements in the self-improvement process itself. This alignment holds in coding, where both evaluation and modification are expressed in the same domain, but breaks down more generally. As a result, prior systems remain constrained by fixed, handcrafted meta-level procedures that do not themselves evolve. We introduce Hyperagents – self-referential agents that can modify both their task-solving behavior and the process that generates future improvements. This enables what we call metacognitive self-modification: learning not just to perform better, but to improve at improving. We instantiate this framework as DGM-Hyperagents (DGM-H), an extension of the DGM in which both task-solving behavior and the self-improvement procedure are editable and subject to evolution. Across diverse domains (coding, paper review, robotics reward design, and Olympiad-level math solution grading), hyperagents enable continuous performance improvements over time and outperform baselines without self-improvement or open-ended exploration, as well as prior self-improving systems (including DGM). DGM-H also improves the process by which new agents are generated (e.g. persistent memory, performance tracking), and these meta-level improvements transfer across domains and accumulate across runs. This work was done during my internship at Meta (@AIatMeta), in collaboration with Bingchen Zhao (@BingchenZhao), Wannan Yang (@winnieyangwn), Jakob Foerster (@j_foerst), Jeff Clune (@jeffclune), Minqi Jiang (@MinqiJiang), Sam Devlin (@smdvln), and Tatiana Shavrina (@rybolos).

Shocker

@Teknium @openclaw OpenAI and Claude are going to completely reverse engineer and steal everyone’s shit,

@openclaw cloning from us*

OpenClaw 2026.3.22 🦞 🏪 ClawHub plugin marketplace 🤖 MiniMax M2.7, GPT-5.4-mini/nano + per-agent reasoning 💬 /btw side questions 🏖️ OpenShell + SSH sandboxes 🌐 Exa, Tavily, Firecrawl search This release is so big it needs its own table of contents. github.com/openclaw/openc…

@VZHydra @kamathematic Bingo

@kamathematic Firecracker is just kvm wrapper You can vibe code it in 2 weeks

"AI infra" would be nothing if AWS never open sourced Firecracker

@mikehostetler It’s the Wild Wild West out here in 2026

Heh, so many on X playing solo ball Nobody ever said this wasn’t a team sport 😎

@fancylancer3991 @supermemory All this means is we need a new benchmark

Recently @supermemory achieved 99% on LongMemEval. The problem is that memory benchmarks were created when LLMs had a very small context window. For example LongMemEval_M is ~1.5M tokens. Which is almost inside the Opus 4.6 context window. From what I understand, current best benchmark is BEAM with 10M context window. So I'm evaluating all new memory systems based on their score in there. Excited to see how @supermemory will score! I am sure it's gonna do well!

@0xSero Bet

In 72 hours I got over 100k of value 1. Lambda gave me 5000$ credits in compute 2. Nvidia offered me 8x H100s on the cloud (20$/h) idk for how long but assuming 2 weeks that'd be 5000$~ 3. TNG technology offered me 2 weeks of B200s which is something like 12000$ in compute 4. A kind person offered me 100k in GCP credits (enough to train a 27B if you do it right) 5. Framework offered to mail me a desktop computer 6. We got 14,000$ in donations which will go to buying 2x RTX Pro 6000s (bringing me up to 384GB VRAM) 7. I got over 6M impressions which based on my RPM would be 1500$ over my 500$~ usual per pay period 8. I have gained 17,000~ followers, over doubling my follower count 9. 17 subscribers on X + 700 on youtube. The total value of all this approaches at minimum 50,000$~ and closer to 150,000$ if I leverage it all. --------------------- What I'll be doing with all this: Eric is an incredibly driven researcher I have been bouncing ideas off of over the last month. Him and I have been tackling the idea of getting massive models to fit on relatively cheap memory. The idea is taking advantage of different forms of memory, in combination with expert saliency scoring, to offload specific expert groupings to different memory tiers. For the MoEs I've tested over my entire AI session history about 37.5% of the model is responsible for 95% of token routing. So we can offload 62.5% of an LLM onto SSD/NVMe/CPU/Cheap VRAM this should theoretically result in minimal latency added if we can select the right experts. We can combine this with paged swapping to further accelerate the prompt processing, if done right we are looking at very very decent performance for massive unquantisation & unpruned LLMs. You can get DeepSeek-v3.2-speciale at full intelligence with decent tokens/s as long as you have enough vram to host the core 20-40% of the model and enough ram or SSD to host the rest. Add quantisation to the mix and you can basically have decent speeds and intelligence with just 5-10% of the model's size in vram (+ you need some for context) The funds will be used to push this to it's limits. ----------------- There's also tons of research that you can quantise a model drastically, then distill from the original BF16 or make a LoRA to align it back to the original mostly. This will be added to the pipeline too. ------------------ All this will be built out here: github.com/0xSero/moe-com… you will be able to take any MoE and shove it in here, and with only 24GB and enough RAM/NVMe to compress it down. it'll be slow as hell but it will work with little tinkering. ------------------ Lastly I will be looking into either a full training run from scratch -> or just post-training on an open AMERICAN base model - a research model - an openclaw/nanoclaw/hermes model - a browser-use model To prove that this can be done. -------------------- I will be bad at all of it, and doubt I will get beyond the best small models from 6 months ago, but I want to prove it's no boogeyman impossible task to everyone who says otherwise. -------------------- By the end of the year: 1. I will have 1 model I trained in some capacity be on the top 5 at either pinchbench, browseruse, or research. 2. My github will have a master repo which combines all my work into reusable generalised scripts to help you do that same. 3. The largest public comparative dataset for all MoE quantisations, prunes, benchmarks, costs, hardware requirements. -------------------------- A lot of this will be lead by Eric, who I will tag in the next post. I want to say thank you to everyone who has supported me, I have gotten a lot of comments stating: 1. I'm crazy, stupid, or both 2. I'm wasting my time, no one cares about this 3. This is not a real issue I believe the amount of interest and support I've received says it all. donate.sybilsolutions.ai

@MyCreativeOwls @Lovable Unless you’re writing a kernel… stop calling it an operating system… it’s called an APP…

Attention @Lovable builders 👷‍♂️ If I created a @lovable personal AI assistant operating system for your business, would you use it? Open source..