Meta open sourced code that lets people build their own Muse AI gadgets using Meta’s new AI agent. The release is built around programming an off-the-shelf ESP32 board with the provided SDKs. As a result, developers can attach Muse to custom displays and input or sensor hardware to create DIY reminder screens and similar devices.
Reassign launched as a 24-hour circular planner that lets busy makers drag to block time, then reshape schedules when plans change. It runs on a 24-hour view with midnight at the top and noon at the bottom. As a result, it syncs bidirectionally with Google Calendar, Outlook, Todoist, and Linear and uses AI through a command palette to rebuild parts of your day live.
Amazon Quick was paired with a deterministic rules engine in a new “Adjudicated Query” pattern to check large numbers of apartment leases against versioned landlord-tenant rules via chat plus a drillable dashboard.
The reference example targets a portfolio of 50,000 leases across multiple states.
As a result, compliance sweeps produce provable completeness receipts and defensible evidence chains, preventing model-based silent narrowing or skipped records while limiting AI calls to an exploratory clause-search path.
Deep Learning Weekly Issue 475 rounds up multiple deep-learning updates including OpenAI’s GPT-6.1 Sol, Anthropic’s Claude Sonnet 5.5, agent tools, MLOps/agent evaluation articles, and two new research papers on LLM agent memory and deep search agents. OpenAI’s GPT-6.1 Sol is positioned at 1/5th the Astra-like price, alongside a new Ultrafast tier at 300 tokens per second. The result is a consolidated guide to what to read next, with specific performance/cost comparisons and research methods (read-time memory curation and role-decoupled iterative synthesis) to apply or evaluate.
Anthropic released Claude Code mods that let developers inject JavaScript/TypeScript functions into Claude Code to change prompts, tool calls, permissions, and even parts of the interface. Mods are enabled by default starting in Claude Code 2.1.287. Developers can now package these customizations as plugins and share live-behavior features like token/context usage displays, but mod code also runs locally so installs from trusted sources matter.
devpit launched as a native desktop app for running Claude Code agents with a watchable terminal view, a card board that triggers real work, and an orchestrator that manages projects together. The launch includes a free test with 20,000 credits. As a result, developers can run these AI coding agents locally-first with visible per-call costs and open-source tooling.
Pi released Pi 1.0, a hardened, minimal, extensible agent harness, and also introduced the experimental Pi Durable package. It adds features including Codemode support for MCP and non-LLM models plus cache warming for Anthropic models. The result is a more durable agent platform with virtual model support, transcript-aware mid-conversation changes, and a default full-screen TUI mode for longer-running tasks.
Qarunbook launched to help users write and manage test plans with options to work through them themselves, with a team, or with AI over MCP. The free plan includes no card and offers 20,000 credits for Proactive AI for Mac. Fixed issues must be retested before they count as passed, changing how test results are tracked.
Earendil and the Pi community shipped Pi 1.0 and also released Pi Durable, an experimental package for building long-running agent frameworks. Pi Durable’s source code is about 15,000 lines (about 150,000 tokens for GPT or about 250,000 for Claude). Pi Durable changes Pi from a single “coding agent” setup into a durability-oriented harness with storage, crash recovery, and concurrent multi-conversation support.
David Heinemeier Hansson said that 37signals has ended routine hand-coding and now uses AI-assisted work as the normal way to build software, triggering debate in the dev community. He pointed to November 24, 2025 as the moment that made coding-with-AI practical (“Opus 4.5”) and said he had retired from being a professional programmer by about March. The company changes its development approach by shifting to more web-first and Rust-backed systems, while arguing that software architecture and engineering practices must be revisited as human hand-coding drops.
Earendil released Pi 1.0, an open-source AI agent software package, and it reached the top spot on Hacker News with 1,000 points. The release adds Codemode with native Model Context Protocol support and other changes aimed at cost and workflow control. As a result, Pi is positioned as stable enough for business use and continues expanding alongside the experimental Pi Durable framework for long-running, crash-recovering multi-user agents.
Bevel launched eu/jev as the second launch from the company, offering a Jev-like System One model hosted in the EU for users blocked by GDPR and data sovereignty limits elsewhere. It runs on Hetzner GPUs in Germany and matches Jev’s pricing, with free output tokens. The result is a GDPR-compliant way to call a Jev-style model without storing prompts or answers.
Pi 1.0 and Pi Durable were promoted via Earendil on Hacker News as part of an AI Engineer NYC announcement roundup. The Latent Space subscriber code gave the first 30 people a 30% discount for new tickets only (no refunds). Pi 1.0 adds features like native MCP support and transcript-aware mid-conversation changes, while Pi Durable moves Pi’s stateful components to TypeScript with checkpoint-based crash recovery and portable, state-synchronized execution.
A tutorial implements Google Research’s Kauldron JAX training library by demonstrating konfig (plain dict configs), kontext (string-key wiring that prevents components from importing each other), ktyping (runtime named-axis shape checks), and kd.train (a readable Trainer), then runs a synthetic CPU training run and a 5-variant config sweep with checkpointing and resume. It installs Kauldron pinned to version 1.4.2 and applies a small compatibility patch because a Trainer can otherwise raise AttributeError before completing a single step. The result is a workflow where changing one config line reshapes dependent values via references and where training components and tensor shapes are validated and rewired through data and strings instead of code edits.
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