Articles tagged "MIT License"

RemoveMacAI is an MIT-licensed CLI that turns off Apple Intelligence on macOS 27 and deletes about 12GB of models. Install, commands, caveats, MDM comparison.

Strata is an MIT-licensed engine running Qwen3.8-Flash-Next (125B MoE) on a 12GB gaming PC: 94 tokens/s on an RTX 5070. Oct 2026 specs and setup.

DwarfStar (ds4) is antirez's MIT local LLM engine for DeepSeek V4, GLM 5.3, Qwen3.8 on Mac and DGX Spark. Oct 2026 guide: memory, setup, Claude Code, speed.

claude-code-templates is a free, open-source (MIT) CLI that installs Claude Code agents, commands, MCPs, hooks, and settings with a single command, and ships a built-in usage-monitoring dashboard. This guide covers its features, installation, usage, and how it compares to Claude Code's built-in plugin system, based only on the official README and repository. Updated Sep 2026.

Paperclip is a free, open-source (MIT) self-hosted app that orchestrates teams of AI agents such as OpenClaw, Claude Code, Codex, and Cursor with an org chart, budgets, and approval gates. Updated Sep 2026.

Hindsight is an open-source (MIT) agent memory system by Vectorize built to create agents that learn over time, not just recall chat history — it hits state-of-the-art scores on the LongMemEval benchmark. Updated Sep 2026.

Whiteboard is an open-source desktop app by YC W26's /dev/fast that draws Claude Code and Codex changes as review diagrams, launched via Show HN in Sept 2026.

MiMo-V2.6 Flash needs ~170-190GB at 4-bit, ~320-350GB at FP8; Pro ~550-600GB at 4-bit; the 9B distill ~6-7GB. Updated Sep 2026: specs, benchmarks, API prices.

WeKnora is an open-source LLM knowledge platform built by Tencent's WeChat team. It turns documents into RAG search, a ReAct agent, and a self-maintaining wiki under an MIT license you can self-host. This guide covers setup, features, and how it compares to alternatives.

Atria Dawn Preview needs an estimated ~400GB at 4-bit, ~780GB at FP8, or ~1,500GB at BF16. A rundown of this 744B MoE model's specs, vendor-reported benchmarks, API options, and comparison with other open MoE models.

GLM-5.3-Flash needs about 190GB VRAM at 4-bit, 740GB at BF16. VRAM and GPU tables for this 320B/18B open MoE, plus local vs API costs. Updated August 2026.

Ornith 1.5 is an MIT open-weight LLM needing 8GB to 800GB VRAM. Compare GPU picks and quantized VRAM needs for 9B, 35B-A3B and 397B. As of August 2026.

Prime Agent treats context as a variable and sub-agents as function calls via its Recursive Language Model design, paired with a Continual Harness you can CRUD-edit. Here's how Prime Intellect's MIT-licensed coding agent actually works. Updated Aug 2026.

OpenChamber is an open-source agentic dev environment on OpenCode, hitting HN in Aug 2026. This guide covers Session Goals, multi-model runs, and setup steps.

LongCat-2.0 is Meituan's 1.6T-parameter, MIT-licensed MoE model. Running it locally needs roughly 3,800GB in BF16 or about 970GB even at INT4 — no personal PC can hold it. Requirements tables and API pricing ($0.75/$2.95 per 1M tokens), updated July 2026.

DeepSeek V4-Flash needs roughly 160GB at 4-bit and V4-Pro about 920GB. The July 31 V4-Flash-0731 build keeps the same footprint while beating V4-Pro on agent benchmarks. VRAM, RAM and GPU tables by quantization, API pricing, and the legacy model retirement. Updated August 2026.

GLM-5.2 needs roughly 430GB at 4-bit and about 1.5TB at BF16 in combined memory. Quick-lookup VRAM, RAM and quantization tables for running Z.ai's 753B / ~40B-active open-weight MoE (MIT-licensed) locally. Updated July 2026.

**[Hunk](https://www.hunk.dev/) is a terminal-based diff viewer purpose-built for code review and AI-agent integration** — a replacement for stock `git diff`, with review-experience as a first-class design goal. **Key features**: (1) **multi-file review sidebar** — see the whole changeset, navigate files, view change counts, (2) **inline AI annotations** — reasoning from AI agents ([Claude Code](https://claude.com/product/claude-code), [Cursor](../columns/cursor-ios-supports-2026-07-01), etc.) shows adjacent to the relevant code, (3) **adaptive layout** — split / stacked / auto-responsive views auto-adjust to terminal width, (4) **watch mode** — auto-refreshes as the working tree changes, (5) **syntax highlighting + themes** — Graphite / Midnight / Ember / Zenburn / Catppuccin, (6) multiple input methods — **keyboard, mouse, pager integration**. **CLI commands**: `hunk diff` (uncommitted changes) and `hunk show` (a commit). **Tech stack**: Node.js 18+, **MIT license**, distributed via **npm / Homebrew / Nix**. Position: the terminal counterpart to [Crit.md's browser PR-review experience](../columns/crit-md-local-first-agent-review-2026-07); alongside [Herdr (parallel agent operations)](../columns/herdr-terminal-agent-multiplexer-2026-07), it's part of the same "terminal-native AI-agent era" trend. **Target users**: developers doing code reviews, teams integrating AI tools into dev workflows, and terminal-first engineers.

**DeepReinforce released Ornith-1.0 on June 26, 2026** ([official](https://deep-reinforce.com/ornith_1_0.html) / [Hugging Face collection](https://huggingface.co/collections/deepreinforce-ai/ornith-10)). It is an **MIT-licensed open-weights family specialized for agentic coding**, **with no regional restrictions**. **Three sizes**: [Ornith-1.0-9B](https://huggingface.co/deepreinforce-ai/Ornith-1.0-9B) (dense, ~19GB BF16) / [Ornith-1.0-35B](https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B) (MoE) / [Ornith-1.0-397B](https://huggingface.co/deepreinforce-ai/Ornith-1.0-397B) (MoE, built on Qwen 3.5 + Gemma 4). **All sizes ship 262K context**, with **FP8 and GGUF quantizations released alongside**. **Benchmarks (vendor-reported, claimed SOTA at each open-weights size tier)**: | Benchmark | 9B | 35B | 397B | |---|---|---|---| | **SWE-Bench Verified** | **69.4%** | **75.6%** | **82.4%** | | **SWE-Bench Pro** | **42.9%** | **50.4%** | **62.2%** | | **SWE-Bench Multilingual** | — | — | **78.9%** | | **Terminal-Bench 2.1** | 43.1% | 64.2% | **77.5-78.2%** | | **NL2Repo** | 27.2% | 34.6% | **48.2%** | | **ClawEval** | — | — | **77.1%** | **Design thesis**: Reinforcement learning optimizes **both the solution rollouts and the scaffolding (the agent structure that drives them) itself** — a 'self-improving' agentic-coding design. It sits naturally next to the [Loop Engineering Maker-Checker](../columns/loop-engineering-ai-agent-paradigm-2026-06) paradigm. Reasoning is exposed via `<think>...</think>` blocks; function calling and tool use are first-class. **Distribution and ops**: vLLM ≥ 0.19.1 / SGLang ≥ 0.5.9 / Transformers ≥ 5.8.1 / Docker + llama.cpp / Ollama. OpenAI-compatible endpoints. The 9B fits on a single 80GB GPU; 35B and 397B want an **8×80GB GPU node (TP=8)**. Agent-framework compatibility: **OpenHands, Hermes Agent, and [OpenClaw](../services/openclaw-setup)** (Oflight's own service line — and ClawEval is in DeepReinforce's published benchmark set). **DeepReinforce lineage**: an RL-focused research organization that has previously shipped [CUDA-L1 (avg 3.12× GPU speedup)](https://github.com/deepreinforce-ai/CUDA-L1), [CUDA-L2 (HGEMM kernels beating cuBLAS)](https://github.com/deepreinforce-ai/CUDA-L2), and **IterX (MLSys 2026 NVIDIA Track)**. Ornith-1.0 applies the same RL playbook to LLM self-improvement. **Positioning**: alongside [Kimi K2.7-Code](../columns/kimi-k2-7-code-moonshot-ai-2026-06) (1T MoE / 32B active) and [GLM-5.2](../columns/local-llm-landscape-2026-june-update) (Intelligence Index v4.1 = 51, open-weights leader), **Ornith-1.0 is at the front of the June-2026 agentic-coding open-weights race**. Against Chinese-origin models (Kimi / GLM), its differentiator is **MIT license + no regional restrictions + a US-flag procurement story**. **Caveat**: benchmarks are DeepReinforce's own vendor-reported numbers. Independent third-party verification on public leaderboards has not yet appeared (as of June 26, 2026). The article closes with **three inquiry funnels for Ornith-1.0–era local-LLM evaluation, build, and ongoing maintenance**.

Kimi K2.5, released by Moonshot AI on January 27, 2026, is a 1 trillion parameter (32B active) MoE model under the MIT License. It scores 76.8% on SWE-bench, 99.0% on HumanEval, and 87.6% on GPQA Diamond. This guide covers its architecture, hardware requirements, Ollama setup, and practical use cases.