AWS reports that Moonshot AI’s Kimi K3.1 Pro Highspeed is now available through Amazon Bedrock, with a 1-million-token context window and support for tool calling, structured output, configurable reasoning, image input, and streaming. It uses the same Bedrock endpoint, APIs, and features as Kimi K3, allowing existing applications to try it by changing the model ID. Moonshot AI claims the post-trained model generates output tokens two to three times faster than Kimi K3.1 Pro and improves agentic and coding tasks, but the report provides no independent measurements.
Google’s Android Developers account published Android Bench results for GPT-6.1 Terra, Kimi K3.1 Pro and DeepSeek V4.1 Flash, according to IT之家. With kimi-code, Kimi K3.1 Pro recorded a 24.0% pass rate and 82.3% average completion across 30 long-horizon tasks, taking 27.4 hours and costing US$317.4 per run. DeepSeek V4.1 Flash, evaluated with opencode, recorded a 17.3% pass rate and 71.2% average completion, with a 12.0-hour average completion time and US$9.9 run cost.
A source-referenced analysis compares how Codex, Gemini CLI, OpenCode, and ZCode assemble model context from system prompts, instruction files, environment details, conversation history, tool output, and new messages. It also examines curated history, prompt caching, context compaction, and OpenCode's image budget. The findings are based on four repository snapshots dated November 9, 2026, with specific files and line numbers cited; the source code and line references may since have changed.
Ollama v0.40.17 adds concurrent generation to its MLX backend and uses mmap for Vulkan integrated GPUs, according to the release notes. The update also improves ChatGPT CLI restore and graceful restart, uses thinking metadata for OpenCode settings, and bumps llama.cpp to b12257 along with MLX and MLX-C.
A Reddit user compared Strata CLI engine 0.1.59 with Basalt 0.3.2 on Windows using Qwen3.8-Flash-Next IQ3S, a 256k context, and vision enabled. The user reported average generation speeds of 99.2 tokens per second for Strata and 100–118 tokens per second for Basalt, while Basalt also used less RAM and produced smoother, more reliable task completion. Basalt's peak prefill speed was reported as 4,200, although the user's average figure was uncertain.
T3 Code has launched the stable release of Orchestrator V2, adding multi-model and multi-harness orchestration to its open-source agentic programming interface. The reported integrations include Codex, Claude Code, Cursor, Grok, OpenCode, Antigravity, Muse Code, and Pi, with other ACP-compatible harnesses available through the ACP Registry.
In a first-hand account, author 老金 describes using Magpie as a local gateway for coding agents such as Codex, Claude Code and OpenCode. The report says Magpie can route requests across providers and accounts, arrange models in ordered fallback groups, and assign models by user intent. Its resource library can distribute MCP tools, Skills, Rules and Hooks to selected clients. The tutorial also covers privacy masking, web search, automatic check-ins, configuration profiles and CLI control. The author warns that two of more than ten imported CC Switch channels did not work and recommends testing imported channels individually.
Meituan’s LongCat team open-sourced LongCat-2.5, a native multimodal model with 1.6 trillion total parameters, 48 billion activated parameters per token, and a 1 million-token context window. The MIT-licensed release includes model weights, a W4A4 quantized version, and deployment guidance based on SGLang-FluentLLM for domestic AI accelerators. Meituan’s evaluation placed the model first in four of 31 comparisons, while OpenCode data cited by Zhidx showed about 109 billion tokens used across roughly 4,700 users and 50,000 sessions.
掘金 · 人工智能 presents Anatomy, an open-source Agent Skill that asks Claude to invent a physical device whose operation represents a technical concept, then renders it as an interactive isometric illustration. It supports SVG, SVG with WebGL, and a beta 3D mode, while using one small model to drive both component motion and displayed figures. The workflow also includes geometry audits and headless-browser screenshot checks, though generation is intentionally slow and 3D remains in beta.
Microsoft released Intelligent Terminal 0.3 with a Headless Mode that lets enabled tabs and panes continue running AI-agent tasks and compilations after the terminal window is closed. A vertical sidebar provides a consolidated view of agent status, approval requests, working directories, Git repositories, branches, and changes, while the release also detects installed agent CLIs and creates profiles for them. The experimental product is available through Microsoft Store and GitHub.
Reddit user /u/Yhojm reported creating a 15-second, 1920×1080, 60fps Shinkansen motion-graphics video with Qwen3.8 Flash Next GSQ RCO IQ3_XXS. The workflow used Strata and OpenCode to direct Three.js, Playwright and FFmpeg. The user said the first render took about 40 minutes and that roughly three follow-up prompts brought the total time to about two hours. On an RTX 4080 with 16GB VRAM, they reported about 95 tokens per second decoding and 3,000 tokens per second prefill. The first render was reportedly dark, lacked buildings and contained too much text.
掘金 describes GitHub Spec Kit as a specification-driven development toolkit that moves AI coding through constitution, specification, plan, tasks, implementation, and convergence stages. Its clarify stage asks about ambiguous requirements, while converge compares generated code with the specification and appends tasks for uncovered gaps; in one reported CLI todo test, this process found an unhandled corrupted-file case despite 14 passing tests. The toolkit supports more than 20 coding agents, including Claude Code, GitHub Copilot, Codex, Gemini CLI, Cursor, Cline, opencode, Qwen Code, Kiro CLI, and Zed.
A Reddit developer proposes training the pretrained Qwen 3.5 4B as a specialized coding agent, beginning with a pilot covering 100–200 functional mini-SaaS applications and tasks such as development, testing, debugging, and maintenance. The developer says supervised fine-tuning alone in earlier work produced complex Three.js scenes, voxel structures, and outputs exceeding 64K tokens, though those results are self-reported and not independently verified. The author is seeking advice on coding domains, agent harnesses, and evaluation methods before proceeding and plans to release weights, datasets, and experimental results, including unsuccessful runs.
devvm is an MIT-licensed, open-source tool that creates an isolated virtual machine for each project and configures the toolchain its files require. Developers can inspect, confirm, or customize a new VM before devvm opens a shell in it, where Claude Code, Codex, or opencode can run. The documentation also describes network tiers, local credential proxying, custom layers, automatic port forwarding, and support for macOS and Linux.
A user loaded Strata and ran the iQ3_xxs quantization of Qwen-Flash-Next on a 16GB M2 Pro Mac mini, setting a maximum context of 128k, and used opencode on a separate workstation to build a script benchmarking performance and output quality on typical tasks such as large CSV sorting and low-end code work at varying context levels. The author reported it worked far better than expected. The author assumes speeds were always at their worst because nothing could be cached and experts were repeatedly re-read and swapped, which would mean prefill and decode token speed stay the same regardless of load. Median speed across tests from 16k to 120k context was 77 tok/s prefill and 5.5 tok/s decode. The author judged it barely passable as a chat generator but not hopeless as a last-resort "living on dial-up" code helper, and said it appears pretty rough on the SSD, though the write load is not much worse than using a Mini as an active DB server or other office server task.
Hugging Face introduced “harness adapters,” LoRA adapters that can be trained for a particular harness and task, including Hermes, Pi, and OpenCode. The company said live reinforcement-learning training was running for LFM2.5, LFM3, and Twil-9B, with training losses available to follow online.
Reddit user ashokallu shared a private, community-modified ChatGPT desktop app that the developer says connects 12 harnesses from the official ACP registry. A main orchestrator can select roles such as investigator, planner, workhorse and code reviewer, then coordinate agents in parallel. The developer also reports adding terminal IDs, real-time token and speed statistics, and an attention alert that beeps for 30 seconds when a task finishes. The developer says an official ChatGPT release once crashed on launch and was fixed locally. The repository remains private, so the claimed orchestration has not been independently validated.
A Reddit user reports running Qwen3.8-27B NVFP4 across two RTX 5060 Ti 16GB GPUs with capy-flash, OpenCode, tensor parallelism, MTP speculative decoding, and an int8 KV cache. The author says connecting both GPUs directly to CPU lanes reduced time to first token by about 2.5 times and doubled batching throughput compared with a chipset-connected card. Their reported configuration reaches 70–95 tokens per second for decoding, while Gen4 x8/x8 performance figures remain estimates.
Switch is an open-source project for deploying AI teammates that work with people in Slack, Microsoft Teams, Discord, Telegram, and Mattermost. The project says each channel can provide shared context for people and agents, while agents can be assigned roles, instructions, tools, and room access. Switch can be self-hosted and is designed to work with different agent harnesses, models, and providers, including Claude Code, Codex, and LangChain-based agents.
TechNews reported that NVIDIA announced a set of free open AI models and tools on November 5 for scientific research, engineering analysis and enterprise applications. Nemotron 3.5 Super VL is designed to analyze screenshots, PDFs and charts, and the report says it offers faster performance than its predecessor. It is available for free through OpenCode, OpenRouter, Hugging Face, Ollama and LM Studio. NVIDIA also expanded its open-source Agent Toolkit with Knowledge Foundation, which allows enterprise AI agents to use internal data for reasoning without moving company data and while retaining control over access and actions. The Kumo model family covers tabular, relational, time-series and predictive workloads.
A community guide reports that Basalt, Unsloth's UD-IQ3_XXS quantization, and the Lamina drafter delivered 195.6 tokens/s average decode and about 5,700 tokens/s prompt reads for Qwen3.8 Flash-Next in an OpenCode session on an RTX 5080. The author says Basalt reached roughly five times llama.cpp's drafted decode speed and seven times its prompt-read speed, while Unsloth's quantization achieved 3.247 WikiText perplexity versus BF16's 3.273. A separate Reddit post says Unsloth disputed the reproducibility of ISTA-DASLab's GSQ-RCO benchmark numbers and found similarly sized Unsloth quantizations performing better in its testing.
HiRoute released an update for its open-source, locally running agent routing engine, adding task-evidence-based trust scoring and per-turn decisions that review the previous, current, and next steps. The release also adds task routing across agents such as Codex, Claude Code, Qoder, Pi, OpenCode, Hermes, and DeepSeek Harness, plus a Windows desktop interface. According to the HiRoute release report, performance and cost figures are vendor-reported and measure routing-policy accuracy rather than task completion rates.
CyberArena evaluated 12 model-and-harness combinations through 22 batches and 466 valid cybersecurity games, with agents attacking opponents while protecting their own services. The research team reported GPT-5.6 Sol running on Codex first with a 78.6 relative score, followed by Claude Opus 5 running on Claude Code at 73.8. Its contrast with static tasks showed capture rates falling to 52%, 37%, and 35% by the third adversarial round for the three models that solved all 30 selected tasks in solo mode.
The developer of the open-source tool optimAIzr says it shipped v0.13.0, adding a VS Code extension that shows live context usage, cache countdowns, five-hour limits and savings inside the editor. The tool tracks and helps optimize usage across Codex, Claude Code and OpenCode, and the developer describes it as free, fully local, requiring no account and uploading no prompts. The idea came from the developer's own analysis showing that rereading context accounted for 56% of their AI usage costs.
A Reddit user in r/LocalLLaMA reports using Qwen3.8 27B Q4 through LM Studio for coding and game modding on a laptop with 20GB RAM, 4GB VRAM and an eighth-generation i7 processor. They measured roughly 0.67–0.77 tokens per second and said some tasks required iterations, while primarily using Pi agent and occasionally Opencode. The account is a single unverified user report; the model identity and performance were not independently validated.
The LoLBench paper introduces a benchmark for evaluating coding agents that must turn community feature proposals into cross-module implementations in large, evolving repositories. Its 100 tasks cover 29 systems, eight languages and five domains, with repositories averaging about 2.4 million lines of code. The paper reports that the best of 28 model-and-scaffold combinations resolved 14% of tasks, while all combinations had a median resolution rate of 2%.
A Juejin author reports that Codex repeatedly modified code and reran failing tests, consuming substantial token usage without completing the task. The article recommends pausing after two unsuccessful attempts on the same issue, preserving evidence, avoiding redundant checks and scope expansion, and recording goals, test results, and remaining work for longer sessions.
Vercel Labs has open-sourced fx, an experimental coding agent and CLI written in Zig that is designed to run as a scripted or embedded tool rather than as a full-screen terminal app, writing conversations into ordinary scrollback history. The same engine is exposed through a CLI and a library, with structured output for automation, Agent Client Protocol connectivity via fx acp, and libfx exposing the engine to JavaScript including a WebAssembly build. Vercel says fx ships as a 6.3 MiB native binary with single-digit MB memory and 10-microsecond cold start, but those figures are vendor-supplied rather than independently benchmarked, and early Hacker News feedback disputed its model-independent claim, with some first-time users reporting inference appeared to run through Vercel AI Gateway.
SimpleEnglish is an MIT-licensed Agent Skill that asks LLMs to write plain English by default and can apply stricter ASD-STE100 rules on request. Its documentation lists compatibility with Claude Code, Cursor, VS Code Copilot, OpenAI Codex, Gemini CLI, Goose, OpenCode, and other Agent Skills-compatible tools. The project reports that version 2.0.1 reduced visible formatting defects by 95% across 16 Claude replies and lowered STE linter violations by 81.3% across nine Claude models, but no evaluation run is available for version 2.1.0 or later.
Baseten announced three infrastructure releases: Grid enters public preview as a gateway, intelligent router, and guardrail layer for coding models; Sandboxes enter public preview as stateful, VM-isolated agent environments; and Loops becomes generally available for serverless supervised fine-tuning and reinforcement learning. The vendor said Grid is compatible with tools including Claude Code, Codex, OpenCode, and Pi, while Loops supports Tinker-compatible workflows and direct deployment through Dedicated Inference. Baseten also cited OpenEvidence, Datadog, and Opendoor as customers using different combinations of its training, inference, and routing products.
What problem it solves
The repository is archived and no longer maintained, with ongoing development continuing under the name Crush. Its archived implementation provided AI-assisted coding, debugging, and related terminal workflows.
Key features
Interactive Terminal Interface — OpenCode provides a terminal user interface for interacting with AI models and managing coding conversations.
Source: RE…
What problem it solves
Coding agents need a structured way to create, manage, and build from Google Stitch designs while integrating with the Stitch MCP server. The project organizes design, code generation, and supporting workflows into skills that can be installed with supported coding agents.
Key features
Design workflows — The design plugin covers converting frontend code into Stitch designs, …
What problem it solves
Making a game requires design documents, art, 3D models, video, code, testing, and publishing, often across several separate tools. OhMyGame brings these activities into one app where an agent can design a game, create its assets, build it, playtest it, and publish it.
Key features
Agent-driven game creation — The built-in agent drafts design documents, generates assets, wri…
What problem it solves
OpenPencil lets users describe UI in natural language and see it appear on an infinite canvas, then modify existing designs by selecting elements and chatting. It also lets multiple AI agents work on different sections of complex pages in parallel while keeping `.op` files human-readable, Git-friendly and diffable.
Key features
Prompt-to-canvas generation — Describe a UI in …
What problem it solves
Developers need a coding agent that can assist with development work while remaining inspectable and open source. The project addresses the need for configurable agents with different access levels, a straightforward installation path across platforms, and a way to explore codebases without modifying them.
Key features
Two built-in agents with different access levels — A def…
What problem it solves
AI coding agents lose context between sessions, so the same decisions get re-explained and nothing about the project is remembered. They also lack a shared workflow and evidence, leaving the human to read every line to confirm what was done. This project adds memory, a workflow, and evidence to agents you already have.
Key features
Persistent project memory with Engram — Eng…
What problem it solves
Developers need a consistent way to control locally configured coding agents across mobile, web, and desktop environments. T3 Code addresses this by providing multiple client applications, support for several agent providers, and remote access to a local server.
Key features
Control from mobile, web, and desktop — Use the mobile, web, or Electron-based desktop application to…
What problem it solves
Coding tools use different component formats, model names, permissions, and discovery rules. Contributors need a shared way to write reusable plugins, agents, skills, and commands once while producing compatible artifacts for each supported harness.
Key features
Multi-harness component support — One collection of plugins, agents, skills, and commands can be used across Claud…
What problem it solves
Coding agents often run with separate dashboards, billing units, and provider defaults, making usage difficult to attribute to an agent, model, or outcome. Teams also need to apply routing, permissions, cost controls, and observability without replacing the harness workflows developers already use.
Key features
Non-invasive agent discovery — The CLI detects supported coding-…
What problem it solves
AI coding assistants can generate interfaces with visual and structural patterns that feel machine-made, such as repetitive headings, empty sections, misplaced heroes, and forms that do not complete their intended action. Hallmark applies design constraints, product-aware page structures, browser-based linting, and fresh-context review to help generated interfaces look desig…
What problem it solves
Agent workflows, corrections, debugging patterns, and repository conventions often disappear when a session ends or remain trapped inside one coding tool. Beacon addresses this by capturing session history across harnesses and turning useful findings into reusable memory for future agents.
Key features
Cross-harness session history — Capture session history from Claude Code,…
What problem it solves
DeepSeek Harness workspaces, tools, and project setup normally remain tied to the computer running the Harness instance. The project addresses the need to continue using that environment from another computer, phone, or browser without requiring a public listening port or public IP on the Host.
Key features
Remote workspace access — Open local workspaces from a web browser, …
What problem it solves
Coding agents run in terminals that die when the client closes, the SSH connection drops, or a machine restarts, so long-running work is lost and the agent that needs attention is hard to find among many panes. Teams also lose sight of which pane is working, blocked, or idle across local and remote machines.
Key features
Detachable background runtime — Terminals keep running…
What problem it solves
AI coding agents often live in separate terminal sessions, lose context when those sessions end, and require repeated supervision. Teams need a shared workspace that connects assigned work, execution, decisions, and results while preserving human review before changes ship.
Key features
Agents as teammates — Create named AI agents with a provider and runtime, then assign the…
What problem it solves
Agent sessions often end without preserving the observations and project knowledge needed for continuity. Claude-Mem addresses this by capturing session activity, generating semantic summaries, and making relevant context available in later sessions.
Key features
Automatic session capture — Claude-Mem records tool usage and other session observations through lifecycle hooks …
What problem it solves
Coding agents can misunderstand requirements, use overly verbose terminology, work without sufficient feedback loops, and accelerate codebase complexity. These skills address those failure modes with repeatable practices that preserve developer control over the engineering process.
Key features
Requirements alignment — Grilling workflows ask detailed questions about a propos…
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