Blog Posts

Harness-1: Teaching Search Agents to Offload State

Search agents and the state problem A search agent is an AI system that answers a question by iterating through multiple searches. Unlike a one-shot retrieval lookup, it reads intermediate results, adjusts its search strategy, compares candidate documents, and checks whether specific claims are actually supported by what it found. Tasks like analyzing financial filings, tracing multi-hop facts across sources, or interpreting complex regulations need this kind of iterative work. A single query won’t get you there.

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Lighter: A ZK-SNARK Orderbook DEX on Ethereum

Decentralized exchanges come in two main forms. AMMs (Automated Market Makers) like Uniswap set prices algorithmically from pool ratios and work as permissionless swap venues. Orderbook DEXes match individual buy and sell orders by price and time priority, the same way centralized exchanges like Binance or Coinbase operate. Orderbooks enable limit orders, tighter spreads with active market-making, and more precise price discovery. AMMs do not.

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LLM Observability Without LangSmith: Five Open-Source Tools Compared

At some point in building LLM applications or agents, you need to know why a call failed, what the tool invocation looked like, or why the agent got stuck in a loop. LangSmith, LangChain’s commercial observability platform, has been the default answer for this: it covers trace visualization, prompt versioning, and evaluation in one place. Its usage-based pricing and cloud-hosted architecture are where teams start looking for alternatives. Traces carry raw user inputs and internal prompts, so shipping that data to an external SaaS is itself a problem for plenty of organizations.

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The AI Agent Trust Stack: ERC-8004, ERC-8126, and ERC-8196

When an AI agent acts on behalf of a user (spending funds, calling contracts, accessing paid APIs), the obvious question is: how do you know this agent is safe? The Ethereum community has three standards that address different layers of that question. They don’t solve the whole problem, but they’re building toward a coherent stack.

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DeepSWE: A Benchmark for Long-Horizon Coding Agents

SWE-bench has been the default coding-agent leaderboard for a while, but it has well-known weaknesses. Most tasks come from existing public issues and PR patches, so a high score might partly reflect memorization. Most tasks are also single-file bug fixes, which is not representative of the multi-file, long-horizon work that a coding agent does in practice.

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Mixture of Agents: How Layering Open-Source LLMs Beat GPT-4 Omni

Instead of scaling a single model up, you can stack multiple models in layers and have each one refine the previous layer’s output. Together AI’s research team formalized that approach in June 2024 as Mixture of Agents (MoA) in arXiv:2406.04692. Using only open-source models, their MoA configuration scored 65.1% on AlpacaEval 2.0, versus 57.5% for GPT-4 Omni.

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Open Knowledge Format: A Shared Vocabulary for Agent Knowledge

When AI agents fail in production, the model is often not the problem. The missing context is. Table schemas, metric definitions, runbooks, join paths between systems, and API deprecation notices are scattered across catalog vendors, internal wikis, code comments, and personal notes. Every agent developer solves the same context assembly problem from scratch.

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Qwen3.6-35B-A3B: Community Reviews, Uncensored Variants, and MTP Benchmarks

Alibaba released Qwen3.6-35B-A3B in April 2026: a 35B-parameter MoE model with around 3B active per token, a 262K native context, and an official SWE-bench score of 73.4%. Two months in, it’s the most widely tested 35B-class model in the local LLM community.

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Robot Learning: A Tutorial (From Classical Robotics to Generalist Policies)

“Robot Learning: A Tutorial” (arXiv:2510.12403) is a paper-length tutorial by Francesco Capuano, Caroline Pascal, Adil Zouitine, Thomas Wolf, and Michel Aractingi, from the University of Oxford and Hugging Face. It covers the full arc of robot learning methods, from classical dynamics-based control through reinforcement learning, imitation learning, and generalist vision-language-action models, using the Hugging Face LeRobot library throughout.

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Secret Voting Architecture with FHE, SP1, and Groth16

On-chain secret voting creates three tensions at once. Votes must stay hidden while still being tallied. Off-chain computation cannot be trusted without proof, yet results need to land on-chain. And the EVM cannot run heavy cryptographic operations natively, but it still needs to verify them. FHE (Fully Homomorphic Encryption), SP1 zkVM, and Groth16 each take on one of these.

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