@Calclavia

Exploring agent swarms @ https://xitter.cf/t.co/ps9MCqTF3C, backed by @spc; Prev. co-founded https://xitter.cf/t.co/PrvwcJieE7, https://xitter.cf/t.co/jUkhv9ulAd (acquired)

calclavia.com
Joined May 2009
  • Tweets6.6K
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  • Followers6.7K
666Media
Smithery has been acquired by Arcade! It’s been an incredible year building Smithery alongside the MCP community. I'm proud that we've built something that helped process millions of tool calls and made 10K+ MCPs from developers around the world more discoverable. My co-founder @kamathematic is joining Arcade to keep building toward our vision of making tool calls the new clicks. As for me, I’ll be taking some time to explore what’s next. More soon!
Arcade.dev | The Actions Runtime
@TryArcadeAug 6
Replying to @SmitheryDotAI
@SmitheryDotAI, the leading public registry and hosting platform for MCP, is now part of Arcade.dev. Smithery made finding and running an MCP server take one click. Pairing that developer experience with the security, reliability, and governance that Arcade is known for, gives enterprises both to lead the next generation of agentic AI. Smithery co-founder @kamathematic is joining us to keep building it. More from @TheMostlyGreat: arcade.dev/blog/smithery-joi…
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Justin D'Souza
@jqdsouzaOct 20
We’re betting our $5.5M seed round that the next leap in AI coding won’t come from OpenAI or Anthropic. It’ll come from a new feedback loop that learns from you. Introducing Beacon: open-source, self-improving memory for coding agents. Your sessions capture how you debug and solve problems. Today, that context gets lost and you have to re-explain things every time you switch tools. Beacon continuously distills your sessions into reusable skills across Claude Code, Cursor, Codex, and 20+ harnesses, so your next agent can learn from the last. Beacon is free and OSS 🌟 github.com/Asymptote-Labs/ag… Why should the frontier labs be the only ones winning with your sessions? You already did the work. Now let your coding agents learn from you.
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Traditional ML is cool again!
Kevin Flansburg
@kevin_flansburgOct 19
Today we introduced a novel system one model, Clairic, based on regression instead of classification (i.e. decision models like Clef). This enables estimation of continuous values without having to exhaustively list choices upfront. It also enables very efficient extraction of structured data from unstructured inputs. Open weights coming soon!
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Before agents: I make algorithms After agents: I make recipes
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june
@arjunkmrmOct 6
Tardigrade is a durable harness framework purely built on Effect primitives. We solved it by write-ahead logging of effects and using event sourcing to derive state. We also used a bunch of cool Effect features like SchemaAST (for incremental JSON validation) to make it more performant than pi-durable on warm turns as events grow.
Michael Arnaldi
@MichaelArnaldiOct 5
Wanted to share a few thoughts regarding the Effect part of this discussion, starting with saying that I do broadly agree with everything that was said and that's exactly why I'd like to add some "maintainer" perspective on the choices.
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When you hire a team of meat proxies:
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june
@arjunkmrmOct 2
/1 Introducing Tardigrade Atoms! ⚛️ Atomic state management for durable cloud agents, powered by @EffectTS_ v4 atoms and event sourcing If you've used @dai_shi's Jotai atoms, this will be a delightful way to manage agent's state and side effects. Everything is an atom! github.com/clavia-labs/tardi…
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It's great that we're seeing more "decision models" aka classifiers being polished
Inception
@_inception_aiSep 30
Introducing Mercury Decide: the most intelligent decision model on @OpenRouter (JevBench v1.4) and one of the fastest. Up to 14 decisions per second. Watch it go head-to-head with Jev in chess. Free for early access on OpenRouter: openrouter.ai/inception/merc…
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Opus 5.5 is now orchestrating my Astras
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june
@arjunkmrmSep 30
If the Effect TS concept of services and layers are confusing, I have a little analogy for you. Imagine you want to make banana cake. An Effect program is the recipe to make one. Services describe the capabilities you need (something to hold, something that can whisk, something that can heat things etc.) Layers describe how to provide these capabilities in specific environments (a box, a whisk, an oven in a kitchen OR something completely make do like a bowl, your fingers, and the sun when outside). Writing your code like this forces you to discover the pure logic of your system. Everything else is layer above this so it can be interpreted by different runtimes.
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People who make RL environments are, in many ways, selling reward shaping as a service.
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My accountants/lawyers could work so much faster if they had access to my Codex. All these email back-and-forths could be mitigated if there were a way to restrict access to specific business context. Instead, all parties are still meat proxying.
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So Anthropic models are good when it's a minor version bump rather than a major version bump!
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@arjunkmrmSep 24
You can now property-test your Tardigrade agents by generating and checking possible trajectories against safety rules! Since each agent is a state machine, we use fast-check to find states that break defined invariants. This is particularly useful in critical settings where you can't simply rely on the vibes of your prompts. This takes us one step closer to formally verifying agent harnesses. Read the complete docs here: tardigrade.sh/docs/verifying…
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@arjunkmrmSep 23
We’ve been using tons of TLA+ for Tardigrade, very bullish on it. Engineers should get into math more than ever because the best specification for a system is math. Not some text in a markdown file. Specifying systems by Leslie Lamport (author of TLA) is a great book for those getting started. lamport.azurewebsites.net/tl…
Boris Cherny
@bchernySep 22
I used Opus 5.5 to formally verify the Claude Agent SDK using Lean. A couple short prompts = 16 PRs fixing various bugs and race conditions. Video attached. TLA+ also works well. I sometimes combine Lean and TLA+ to look for issues around data flow, concurrency, and state mgmt. I don't know either language well, but Claude is excellent at both. This approach is super useful for formally modeling your code and finding bugs that a human probably wouldn't have spotted. Is formal verification the future of coding (or at least, bug finding)?
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Multi-agent simulations are going to be prevalent for RL envs!
june
@arjunkmrmSep 23
Tardietown! 👋 A fun experiment where you create a town of agents coordinating using a reddit-like forum with karma for work. Still very rough around the edges. Working on it mainly as a demo for tardigrade. Everything in the town is an actor hosted as a durable object; the residents, library, packages etc. Open sourced. Run it locally, deploy it, clone it and create your own towns and simulations! github.com/clavia-labs/tardi…
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I built Vybez A small TypeScript sugar that weaves Jev decisions ergonomically into your existing code. Most workflows mix semantic decisions and deterministic rules; Vybez makes that strongly typed and seamless in TypeScript. github.com/clavia-labs/vybez Try: bun add vybez
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Inspired by: xitter.cf/southpolesteve/status/…
Steve Faulkner
@southpolesteveSep 18
I've seen people describe Jev as an "AI if statement". But what if it actually WAS an if statement? Introducing Probably: a programming language powered by Jev: probably-lang.southpolesteve… Jev baked into the language. “feels” asks a question. “match” routes between descriptions. “while” keeps going until something stops feeling true. This is obviously a toy, but it's fun to think about what something like Jev unlocks. Jev makes the decisions, an LLM does the writing, and a little program ties it together.
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So far, I'm impressed with Jev as a verifier and reward model. It makes RL for hard-to-verify domains so much cheaper to iterate! @typesafeai Allows you to easily implement CheckEval-style judges: arxiv.org/abs/2403.18771

CheckEval: A reliable LLM-as-a-Judge framework for evaluating text...

Existing LLM-as-a-Judge approaches for evaluating text generation suffer from rating inconsistencies, with low agreement and high rating variance across different evaluator models. We attribute...

arxiv.org
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One trick to get "reasoning" out of Jev @typesafeai is to loop it: feed Jev's output score back into its input state, and iterate a few times to refine its confidence. It's the Universal Transformers (arxiv.org/abs/1807.03819) idea from 2018.

Universal Transformers

Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their...

arxiv.org
1Broom
@The1BroomSep 20
Replying to @Calclavia @typesafeai
The dial I want is not more compute on every call, it is the option to spend more only on the unsure ones. I already branch on confidence in my game. Let me hand the 0.5s back for a second, harder look and leave the 0.9s alone.
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Calling Jev @typesafeai system one limits our imagination of what classifiers can do. I can easily see these models have a reasoning dial to trade more compute for performance. In that sense it becomes more than just system one.
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