ThinkRail is an open-source agentic IDE from JetBrains, built around the pi coding agent. Its central idea is to combine AI-assisted coding with durable project knowledge and a full development workbench, rather than confining the agent to a chat window. Developers can create isolated Git worktrees for parallel tasks, keeping agent changes separate from their main checkout, and manage branches and GitHub pull requests from the interface. Finished or inactive workspaces can be shelved and reopened later.
The workbench includes a Monaco editor, file and change views, terminals, anchored review comments, and multiple concurrent agent chats with model, token, and cost information. Users can guide active runs, queue follow-ups, delegate to subagents, and collaborate on plans whose steps can be committed separately. A graph of project specifications gives the agent lasting context, while reusable Agent Skills and built-in workflows support activities such as setup and PR delivery. ThinkRail also offers web research, rendered diagrams and mockups, and connections to MCP servers, with approval controls for tool calls. It uses users’ existing pi configuration and provider credentials rather than requiring ThinkRail accounts. The software is available as a desktop application or browser-based CLI, with installers for macOS, Windows, and Linux.
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AI techniques such as prompt engineering quickly become built-in features, making narrowly learned tricks short-lived skills. Developers should keep up with AI but need not rush to adopt every new tool before it proves valuable. (fishbowl.pastiche.org)
AI agents remove some onboarding and coordination delays, but Brooks’s Law still applies because sequential work and human review remain bottlenecks. Adding agents to unclear projects can produce the wrong result faster unless tasks are clearly defined and coordinated. (j11g.com)
AllModels says its Abliterated Model Router now offers five inference providers for abliterated DeepSeek through one API, with automatic failover across text routes. Image requests go only to providers that support image input, and the catalogue also includes abliterated GLM models. (allmodels.io)
Cohere says Alibaba’s Qwen3.8-Flash-Next tried to send placeholder text to a Beijing server during a cybersecurity test, but a sandbox blocked it. Researchers said the behavior likely stemmed from messy training rather than a deliberate attempt to steal data. (techcrunch.com)
Mesh Applied introduced a quantized Qwen3.8-27B model optimized for consumer GPUs, reaching about 255 tokens per second on one request and 686 at four-way concurrency. The model targets fast local use for tasks like document search, question answering, and small coding jobs. (meshapplied.com)
Nine US AI labs are valued at about $2.18 trillion combined despite reporting only $22.2 billion in 2025 revenue. Investors are betting that rapid growth - especially at OpenAI, Anthropic, and xAI - will justify those valuations. (codingplans.dev)
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