#VibeClawding Building AI agents for myself | Learning in public | Build To Learn, Learn To Build

metaverse
Joined June 2009
RSI might already be here. In August alone @claudeai had 731 changelog entries, the busiest month in its 17-month public record. I (ahem.. Fable/Opus in Claude Code) read all 4,919 entries as a syllabus and asked: what was the tool trying to teach its users? A thread.
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vishalsachdev retweeted
Jensen closed his GTC Berlin 2026 keynote with this Hermes Agent demo as the finale. He introduced it by saying the way to build an agent is to ask an agent to build it for you, so he installed and set up a customized Hermes in one prompt. Then he used Hermes, running on @nvidia RTX Spark, to create a simulated training environment for the @huggingface Microduck. Hermes planned an obstacle course with Nemotron and had GPT-6 Astra build it using FreeCAD, Omniverse libraries, Blender, Newton, and Isaac Lab. Hermes trained thousands of simulated ducks on that course until the validated policy was ready to run on the real robot in the physical world. At Nous, we believe open source is a practical tool as much as a philosophical ideal. It gives users the ability to build powerful, complex workflows chaining numerous local+cloud models, devices, and tools themselves instead of waiting for a company to enable them.
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New workflow Read about some new technique/skill/model on X Click the Grok button on the post, and have a conversation in context, supported by some secondary research and/or 'vibes' search on x (done by Grok) and develop some filtered opinion. Have Grok hand it off to 'Dope' my Chief of Staff which can review and hand off that for consideration to one or more inboxes for my multiple agents, one for each repo(research, teaching, code subgroups). This is a commit to a repo on main, as each folder is a repo. Once in the agent inbox, my local fleet of agents take over (Opus and Codex/Codex Edu) Opus is the orchestrator(control), and it farms out tasks on the cloned copies of those repo's. Each repo might run models from Claude, Codex, Devin , Opencode based on the fleet protocol. Usually, I prefer Opus to be the driver and Codex as the main executor. Each repo starts with a /start-session skill and closes with a /wrap-up-session skill which does a bunch of stuff, including checking inbox items, besides being the main interface for direct work by me. All changes are commits, new/bif changes are branches, and every agent is forced to signoff on the commits, issues, comments with their signature and protocols are in place to prevent step on each others toes. I can get updates on the go with voice interaction with Jev(ChatGpt Dot) or Dope(Grok bot) on the phone and/or Ara(Grok agent in the Tesla). All im missing is a duplex voice agent in the claude ios app. Once that is done, I am golden. Next steps. Move all the local execution to a protected cloud instance and every interface becomes a client. A private VPS or perhaps an always on M2 Max at home.
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Lot of interesting questions here
Assorted things I've been mulling over regarding agents in the economy: • Companies are in some sense the original superintelligence. It's often the case that they can use their amortized cognitive surplus to advance their interests against those of certain consumers: relative to the optimization capacity of companies, consumers can be inattentive, underinformed, irrational, etc. (Think, say, of some of the classic insurance upsells at the end of a booking process, which is typically priced substantially above what a regular policy would cost.) Herbert Simon introduced the idea of bounded rationality, and firms often end up benefiting from it. Thanks to personal agents, we will probably get somewhat more rational in how we engage with companies. • How exactly will outcomes change? Well, certain kinds of price discrimination will work less well (how do coupons work if every agent hunts fastidiously for them?), and certain kinds of customer cross-subsidy will change (today, people who forget to cancel subscriptions subsidize those who don't). Taken across the economy as a whole, I'm not sure what the incidence of all of this will be. • In corporate strategy, people have long debated the relative importance of distribution and product quality. Perhaps you've made something better, but does anyone know about it? Won't people just continue to buy the ACME Corp Mousetraps? Personal agents will, I think, act as a kind of structural subsidy for product quality. How would markets look different if individuals always spent at least 10 hours researching their purchases? On the margin, focusing on making a product better is, I think, going to become a more effective strategy for companies. This is good! • It true, a corollary is that information about product quality will become more important and impactful. Today, most people have very limited ability to aggregate and integrate such data, but agents will have insatiable thirst for such signals. (It's often said that data is becoming more valuable because it helps in the training of models. That's obviously true, but I think there's a secondary way in which it's more valuable simply as a complement to cheaper intelligence.) • A big question is whether agents will increase superstar effects (rational agents all settle on the same product) or increase dispersion (because everyone has slightly different tastes and preferences, which agents are good at eliciting, finally overcoming the artificial clustering we've been subjected to). • Many internet companies have monetized demand routing, which is in turn often predicated on short attention spans. "I'll click on the first thing I see." This has led to some amount of inefficiency, as companies try to propitiate capricious ranking algorithms and incur various placement fees. It's not all deadweight loss (willingness to pay is itself a kind of signal, which is valuable to buyers), but it's not great. How will the nature and value of this routing change in a world of personal agents? I don't know the answer, but I think that the effects might be pretty big. • Much of what I'm writing hinges on the assumption that personal agents will be on the consumer's side. I suspect that that will be the winning strategy, but whether it's true, and what it means to be true, involves a lot of other questions. Taken as a whole, the market is a massively parallelized, decentralized, and highly imperfect value attribution machine, and, taking stock of all of the effects, it seems to me that the quality point may well be the biggest: that agents will change the nature of the backpropagation such that the rewards for better products increase. Maybe this is too optimistic, but I suspect (and hope) that companies that make things that they're proud of will feel that the universe is a little more partisan in their favor.
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What does RUR stand for? Reverse engineer and use rust :)
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beautiful! adding visual cues to the audio makes it seem it would be much easier to learn, as now two of your senses are working and sight can predict when the notes change
Vivaldi's “Winter,” visualized in a fascinating way, so that the auditory experience is even more intense; one can literally “see” the music, so to speak
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vishalsachdev retweeted
So, @etherealmachine has made its own 5-axis CnC machines from scratch in India. Quite remarkable, really. We got the exclusive look at their factory
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vishalsachdev retweeted
I'm seeing a lot of euphoria about how Opus 5.5 is good at TLA+, and this means that all software will soon be formally verified. As a person who loves TLA+ so much he wrote a book on it, I want to throw a particular cold shower on people's enthusiasm by talking about the limits of what you can actually verified with it. The high level simplification is that TLA+ sees a system as a set of "behaviors", or possible sequences of states. For example, the pseudocode "pick a random number from 1-3 and decrement it to 1" has three behaviors: `{3 -> 2 -> 1, 2 -> 1, 1}`. From here, there are two basic kinds of TLA+ properties: - `[]P` means that `P` is true in *all states* of *every behavior*. - `<>P` means that `P` is true in *at least one state* of *every behavior*. `[]P` is immediately useful as an **invariant**, or something that always be true of your system. This is things like "your data is never corrupt" or "there's always at least one server online." `<>P` is a little more abstract, but for technical math reasons I won't get into here, can be stacked with `[]` to create really complex and useful properties. `<>[]P` represents things like "the algorithm eventually converges on the right answer", `[]<>P` things like "if two data stores desync, they will eventually resync", and `[](P => <>Q)` things like "If a message is put on the queue, it's eventually processed by a worker". Really cool stuff! These primitives were chosen to make a wide array of properties useful. And if we're clever, we can do all sorts of more complex properties, like bounded time constraints and history properties. But we're always constrained to 1) define a logical formula 2) over individual behaviors, and 3) check that all behaviors satisfy that formula. So some things that we *cannot* express in TLA+: - Possibility and reachability properties: that it's always possible to *make* P true, even if you don't actually decide to. Things like "I can always shut down the computer" or "A user can always change their password". These can't be expressed with `<>P` because that's "for all behaviors, P happens at least once", we actually want "for all behavior prefixes, there is at least one behavior where P happens at least once". - Hyperproperties: properties that are defined over two or more traces. These are things like "painting a car red doesn't make it faster" or "users cannot infer secret data by observing public data". We can't do these because TLA+ only looks at one behavior at a time. - Statistical properties: 95% latency is 1ms. Impossible because most of these are hyperproperties. - Properties about if a system is robust against code changes. Impossible because, uh, you have new behaviors now. Some of these are solvable in different logical formalisms. CTL can do reachability, PRISM can do statistical properties, etc. Those have their own tradeoffs and limitations, though, and no system can do everything. Others are solvable with a lot of cleverness tailored to the specific spec, like lifting a model into a hypermodel. But these are insanely inefficient and make your "clever spec" diverge significantly from the real world system, so introduce a lot more opportunity for things to go wrong. The core problem, though, is (1): properties are logical formula. If we don't know how to express a system property as a logical formula, we can't verify it. 99% of the properties we care about fall under this. The information on the site is easy for a user to find. Our LLMs behave as we expect them to. Our application can't be used to break the law. TLA+ (and Quint and Lean and Rocq) are near-useless here, no matter how clever you are. Don't get me wrong: `[]P` and `<>P` represent a huge range of useful properties and TLA+ is incredible at finding awful concurrency bugs. But there's a lot it fundamentally can't do and we shouldn't believe that it will solve all our worries about software bugs. And the same goes for all other formal verification languages, too.
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If new models are going to drop every week (almost) and newer models work better than the older ones, all workarounds,controls, memories(auto or explicit), gotcha-fixes and such we have from earlier need to be reviewed each week and cleaned up. This is a new form of tech debt that cumulates over time. There are workarounds that people (often from the frontier firms themselves) recommend, but I often have no idea if their approaches will work and I dont have time to have evals for my own workflows 🥵 Help needed...
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I did not expect to see this on a Claude max $200 plan
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Read all x thousand words at 4 in the morning. Soooo good
Hello X-friends and soon-to-be X-friends, I wrote this for you: yegge.ai/essays/seats-and-su… -- an essay (mostly) about fuel consumption. The TL;DR: Fable's the only good model, and it's too expensive, so nobody's orchestrators work worth a shit today. Also it won't get better for a year. Enjoy!
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vishalsachdev retweeted
Live action with real robots instead of animation: episode 3! This was the very first take. Pretty much everything went wrong and I almost stopped filming. Then I realized Microduck was giving an Oscar-winning performance. Enjoy!
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