AI Coding Live News

Factory launches Droid 1.0: programmatic tool calling by default, hundreds of tool calls per model request, leads native harnesses on its own Terminal-Bench 4 runs

AI coding agent company Factory announced Droid 1.0 on X and its blog at 22:31 UTC on Oct 29, calling it the largest update yet to its agent harness. The key change makes programmatic tool calling (PTC) the default: the model writes a JavaScript program that runs up to hundreds of tool calls in a sandbox in one model request. In Factorys own Terminal-Bench 4 runs, Droid scored 57.6% with GPT-6.1 Sol ($0.79 per passed task) and 65.2% with Claude Opus 5.5 ($4.73), ahead of Codex, Claude Code, Cursor, OpenCode and Devin on the same models.

Factory launches Droid 1.0: programmatic tool calling by default, hundreds of tool calls per model request, leads native harnesses on its own Terminal-Bench 4 runs

Key Takeaways

  • Programmatic tool calling is now the default: the model writes JavaScript that runs in a sandbox with no file, network or system access of its own, making up to hundreds of tool calls per request, with independent calls run concurrently
  • Factory example: moving and verifying 151 Linear issues took 312 tool calls; direct calls needed about 36 requests and 3.1M input tokens, a Script needed 2 requests and 61K tokens
  • Terminal-Bench 4 (Factory runs, Oct 23-28, three trials per task): with GPT-6.1 Sol, Droid 57.6% vs Codex 54.0%, OpenCode 51.0%, Devin 45.0%; with Opus 5.5, Droid 65.2% vs Cursor and OpenCode 62.1%, Devin 59.6%, Claude Code 57.6%
  • The first request shrank from 20,887 to 13,055 tokens (-37%) and up-front tool definitions went from 11 to 2; newly loaded MCP tools are appended to keep the prompt cache intact, and a typical production request reads 98% of input from cache
  • Available now as a macOS and Windows desktop app, a terminal UI and the Droid SDK (@factory/droid-sdk); the benchmarks are vendor-run with no third-party reproduction yet
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MLExpert: Open 35B MoE Agentic Model Lifts MLE-Bench Lite Medal Rate from 37.9% to 72.7%, Matching Kimi-K3 and GLM-5.3

Advancing autonomous AI-for-AI (AI4AI) engineering, researchers from Shanghai AI Lab (InternScience) release MLExpert (arXiv:2610.22344), a 35B Mixture-of-Experts agentic model for autonomous machine learning engineering. Powered by the Kaggle-MLE Factory, hierarchical agentic SFT, and Windowed Interaction RL (WIRL), MLExpert boosts average medal rates on MLE-Bench Lite from 37.9% to 72.7%—matching frontier giants like Kimi-K3 and GLM-5.3 while generalizing to agent scaffold optimization.

MLExpert: Open 35B MoE Agentic Model Lifts MLE-Bench Lite Medal Rate from 37.9% to 72.7%, Matching Kimi-K3 and GLM-5.3

Key Takeaways

  • 72.7% medal rate on MLE-Bench Lite: boosts base model performance from 37.9% to 72.7%, matching frontier models Kimi-K3 and GLM-5.3 with a 35B MoE backbone
  • Windowed Interaction RL (WIRL): assigns shared outcome rewards to multi-turn windows between successive experiment results with asynchronous policy updates
  • Cross-domain AI4AI generalization: transfers optimization skills directly to NatureBench Lite, agent scaffold optimization, and algorithm engineering
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AgentField / Santosh Radha@Agent-Field·
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AgentField open-sources CodeAF v0.6: one Go binary software factory for open models; vendor DeepSWE 54.9% #1 same-model

CodeAF is an Apache-2.0 Go binary that runs open-weight models as a local software factory with task fan-out and Pareto crewing. v0.6.0 shipped 2026-10-01; ★259 now. Vendor DeepSWE doc: /senior-dev solved 62/113 (54.9%) on DeepSeek V4 Flash vs nine other harnesses—with stated one-seed and sampling caveats.

AgentField open-sources CodeAF v0.6: one Go binary software factory for open models; vendor DeepSWE 54.9% #1 same-model

Key Takeaways

  • Agent-Field/CodeAF Apache-2.0; ★259 / 34 forks; install via agentfield.ai/get/codeaf
  • v0.6.0 published 2026-10-01T18:59:00Z; README still says early preview
  • Vendor DeepSWE same-model table: senior-dev 62/113 (54.9%) at ~22¢/task vs nine harnesses—one seed; sampling-contract caveat documented
  • Later same 113 tasks: 88/113 (77.9%) on DeepSeek V4.1 Flash; 78/113 (69.0%) on Kimi K3 (senior-dev only)
  • Built-in providers include OpenRouter, DeepSeek, GLM, Kimi, MiniMax, Qwen, Codex, Ollama; default worker/planner/checker crew
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huggingface_hub 2.0.0: HTTP stack moves to httpx2; all 1.x deprecated APIs removed

Hugging Face released huggingface_hub v2.0.0 on 2026-09-24: the HTTP stack switches from httpx/httpcore to httpx2/httpcore2, so custom client factories and transport except clauses must use the new types; all 1.x deprecated APIs (upload_large_folder, huggingface-cli, etc.) are removed with an official migration guide.

huggingface_hub 2.0.0: HTTP stack moves to httpx2; all 1.x deprecated APIs removed

Key Takeaways

  • Breaking: clients/exceptions come from httpx2/httpcore2 (release v2.0.0)
  • Compat import: from huggingface_hub.utils import get_session, httpx (works from v1.30.0)
  • OS trust store by default; loggers httpx2/httpcore2; oauth needs authlib>=1.8.0
  • Removed APIs: upload_large_folder→upload_folder, huggingface-cli→hf, etc.
  • Upgrade: pip install -U "huggingface_hub>=2.0.0" + migration guide
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Factory launches Teams self-serve: $60/mo per team + $40/mo per seat (up to 10), centralized billing for Droids

On 2026-09-23 Factory announced Teams: multi-user self-serve with centralized billing so Droids take bugfixes/tests/migrations. Price: $60/mo per team + $40/mo per seat, max 10 seats, each user keeping Pro rate limits. Teams page offers a selective $500 voucher.

Factory launches Teams self-serve: $60/mo per team + $40/mo per seat (up to 10), centralized billing for Droids

Key Takeaways

  • $60/mo team + $40/mo seat, max 10 (Pricing)
  • Per-user Pro rate limits preserved
  • Delegate fixes/tests/migrations to Droids (Teams)
  • Selective $500 voucher via Teams application
  • Keep Private/VPC for air-gap/compliance needs
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Factory Private: run Droids in your VPC, on-prem, or air-gapped networks

Factory introduced Factory Private so organizations can run autonomous software-engineering Droids in their own VPCs, on-premises environments, or air-gapped networks. Alongside Factory Managed and FedRAMP (authorization in progress), it gives enterprises more control over where agents run and where data resides.

Key Takeaways

  • Official @FactoryAI launched Factory Private for VPC, on-prem, or air-gapped deploys.
  • Three deployment models: Factory Managed, Factory Private, and Factory FedRAMP (authorization in progress).
  • Targets enterprise data residency and control over where Droids run and data lives; details at factory.com/news/factory-private.
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Enterprise Software Agent Platform Factory Raises M at B Valuation

Autonomous enterprise software development platform Factory secured million in fresh funding, reaching a billion valuation. Its specialized Droid agents handle end-to-end Jira tickets, write code, run CI/CD tests, and manage PR reviews autonomously for global enterprises.

Key Takeaways

  • B valuation underscores massive enterprise appetite for unattended software engineering
  • Droid agents interface directly with Jira, GitHub Enterprise, and corporate service meshes
  • Proves task-oriented autonomous coding agents command premium enterprise contracts
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Factory for Slack Code enters select beta: ship software tasks in-channel

Factory announced Factory for Slack Code is in beta for select users. Teams can bring a software task into a Slack channel, collaborate there, and have Factory do the coding work—with results returned to the same conversation, embedding agentic coding into existing team chat.

Key Takeaways

  • Factory for Slack Code is in select beta for early users.
  • Software tasks can be filed and discussed in-channel while Factory executes the coding work.
  • Results return to the original thread, keeping agentic coding inside team chat.
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Factory raises $200M at $5B valuation to scale self-improving enterprise software development

Factory (@FactoryAI) announced a $200M raise at a $5B valuation (over $400M total funding), more than tripling its $1.5B April valuation, to scale self-improving software development and autonomous coding agents (Droids) for the enterprise.

Key Takeaways

  • Factory raised $200M at a $5B valuation.
  • Total funding tops $400M; valuation more than triples the $1.5B April mark.
  • Capital targets enterprise self-improving software development and coding agents.
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Claude Marketplace adds CrowdStrike, Cursor, Factory, Gamma, Vercel — buy with Anthropic spend commitment

Anthropic announced new Claude Marketplace listings: CrowdStrike, Cursor, FactoryAI, Gamma, and Vercel. Enterprises can apply Anthropic spend commitments toward more Claude-powered products and agents.

Key Takeaways

  • New listings: CrowdStrike, Cursor, FactoryAI, Gamma, Vercel.
  • Enterprises can spend Anthropic commitments on Claude-powered products/agents.
  • Start at claude.com/platform/marketplace.
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Factory: GPT-6 Astra now available for all customers

Software-engineering agent platform Factory says OpenAI GPT-6 Astra is available to all customers. After Copilot, Cline, and v0 product hooks, Factory adds Astra to its agent coding workflows for long-horizon delegation and open-ended problem solving.

Key Takeaways

  • Access: GPT-6 Astra is available to all Factory customers.
  • Use case: long-horizon software-engineering agent delegation.
  • Ecosystem: joins same-day IDE/agent product rollouts of Astra.
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Command Code: GPT-6 Astra is live

CLI coding agent Command Code says OpenAI GPT-6 Astra is live. Developers can pick Astra inside Command Code workflows for stronger long-horizon agentic coding, complementing same-day product rollouts in Copilot, Cline, v0, and Factory.

Key Takeaways

  • Access: GPT-6 Astra is live in Command Code.
  • Form factor: terminal/CLI coding-agent workflows.
  • Ecosystem: adds a CLI-agent entry point alongside other day-of Astra hooks.
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Factory Droid adds Claude Fable 5.1 for open-ended problem solving and large-scale delegation

Factory (@FactoryAI) says Claude Fable 5.1 is now in its coding agent Droid—best for open-ended problem solving, tech-debt clean-up, and merge-ready large-scale delegation—wiring Anthropic's latest high-effort coding model into the Factory workflow.

Key Takeaways

  • Claude Fable 5.1 is now selectable inside Factory Droid.
  • Recommended for open-ended problem solving, tech-debt clean-up, and merge-ready large-scale delegation.
  • Brings Anthropic's high-effort coding model into the Factory agent workflow.
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Cursor Can Now Create Web Apps From Scratch, Store Code in Origin, and Deploy to Vercel

Cursor launched a start-from-scratch flow: its coding agent can create new web apps, persist the code in Origin, and deploy straight to Vercel. That moves Cursor from an IDE that edits existing repos to an end-to-end app factory—generate, host, and ship—cutting the path from idea to a live URL.

Key Takeaways

  • The new flow covers create, store, and deploy—no pre-existing local repo required
  • Origin is Cursor’s code store, closing an official loop with Vercel deploys
  • Coding agents are shifting from pair-programming inside a repo to prompt-to-production scaffolding
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Fireworks opens Training API and Fireworks Lab for serverless or dedicated RL at scale

Fireworks announced general availability of its Training API and Fireworks Lab, aiming to make model specialization accessible beyond labs that already own training clusters. The API lets teams connect a custom Python training loop to Fireworks-managed distributed training and rollout infrastructure, keeping control of loss, reward, data, and environment. Compute is split into Serverless mode for fast experiments and Dedicated mode when scale, control, and GPU economics matter. The stack is built for reinforcement learning as a single train-sample-serve-retrain loop, with three hard requirements: rollout throughput, weight sync so fresh weights land on the rollout deployment every step, and correctness so trainer and inference engine agree on the same model. Fireworks Lab additionally embeds forward-deployed researchers and engineers to co-design or build a model. Named customers include Cursor, Harvey, Figma, Vercel, Cognition, Factory, and Heidi.

Key Takeaways

  • The Training API attaches a custom Python loop to managed distributed training and rollout, with Serverless and Dedicated compute.
  • The RL loop is optimized for rollout throughput, per-step weight sync, and trainer-inference model agreement.
  • Fireworks Lab embeds researchers with customers including Cursor, Harvey, Figma, Vercel, Cognition, and Factory.
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