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DeepSeek Code is an open-source CLI built specifically for DeepSeek — one of the most cost-effective AI models on the market! It was heavily inspired by tools like Claude Code and OpenAI's Codex for most of its feature set. Since it is fully open-source, you are more than welcome to open Pull Requests, report bugs, or submit issue suggestions! Installation You can install it globally via npm or bun: `` Using npm npm install --global @hermenics/deepseek-code Using bun bun add -g @hermenics/deepseek-code `` Check out the repository, star the project, or contribute: 👉 GitHub: https://github.com/Hermenics/deepseek-code Feel free to test it out and leave your feedback below! PRs and Issues are highly appreciated. ai #cli #showdev #typescipt

GitHub热门项目 | Open source agentic operating system | Stars: 19,064 | 115 stars this week | 语言: TypeScript

GitHub热门项目 | local multi-agent harness | Stars: 1,158 | 200 stars today | 语言: TypeScript

GitHub热门项目 | The first vision plugin for DeepSeek Harness, and the vision bridge for every text-only coding agent. Paste an image, get structured JSON evidence (OCR, layout, semantics). | 全网最强 DeepSeek Harness 外挂视觉插件，为 DeepSeek、GLM 等纯文本模型外挂视觉能力，粘贴图片即得结构化 JSON 证据（OCR、版面、语义）。 | Stars: 2,222 | 590 stars today | 语言: TypeScript

GitHub热门项目 | BetterGI 的脚本仓库，内含BetterGI 的JS脚本、路径追踪、战斗策略、七圣召唤策略。 | Stars: 515 | 2 stars today | 语言: JavaScript

GitHub热门项目 | یک پنل گرافیکی کاربردی برای ارائه اشتراک‌های Worker با پروکسی‌های ، Trojan و Warp به همراه زنجیره پروکسی، ارائه دهنده تنظیمات کامل DNS، IP تمیز و روتینگ پیشرفته برای کاربران تمامی پلتفرم‌ها با استفاده از هسته‌های Amnezia، Wireguard، Sing-box، Clash/Mihomo و Xray. | Stars: 3,039 | 24 stars today | 语言: JavaScript

GitHub热门项目 | Grok open release | Stars: 52,138 | 13 stars today | 语言: Python

GitHub热门项目 | Beautiful, Modern & Opinionated Linux | Stars: 25,105 | 225 stars today | 语言: Shell

Ask a modern chatbot to help with something completely ordinary and there is a growing chance it will decline . Not because the request was dangerous, but because it brushed against a keyword, a topic, or a category that the vendor's safety systems treat as radioactive. A recipe that mentions alcohol. A history question about a violent event. A medical query you were entitled to ask. A creative scene with any conflict in it. The refusal arrives politely, firmly, and without much interest in whether it was warranted. Safety is real; this is not most of it Let us be fair, because this is a topic where fairness is usually the first casualty. Some restrictions are entirely sensible. Refusing to help synthesise a weapon, produce material that sexualises children, or plan real violence is not censorship; it is basic responsibility, and reasonable people want it there. The complaint is not about those lines. It is about everything on the wrong side of a border that has been drawn far too wide, catching countless legitimate requests to avoid a handful of genuinely bad ones. There is a difference between refusing to help build a bomb and refusing to discuss the chemistry a GCSE student is studying. Too many systems can no longer tell which one you are asking for. Whose values, decided by whom There is a question underneath the practical annoyance that deserves stating plainly: when a model refuses, whose standards is it enforcing? The boundaries of what these systems will and will not discuss are set inside companies, by people you did not elect, according to policies you cannot read, calibrated to a mixture of genuine safety concern, legal caution and brand protection. A handful of firms are, in effect, quietly setting the terms of acceptable enquiry for hundreds of millions of people, and doing so through refusals that arrive without an appeal, an explanation of the rule, or any way to contest the judgement. Reasonable people disagree about difficult topics, and different

La mayoría de los "agentes con memoria" recuerdan datos : un string, un hecho, una preferencia. Pero cuando hablas semanas con un agente, lo que quieres no es una base de datos con tus notas. Quieres que recuerde vuestra historia : el día que conociste a su perro, la vez que os reísteis de una idea absurda, lo que prometisteis hacer juntos. Y, sobre todo, quieres que no confunda lo real con lo soñado . Por eso construí OurBook : un servidor MCP de memoria narrativa . El agente no guarda tus datos: guarda vuestra vida en común, con emoción, con un diario que se escribe cada noche y con sueños que consolidan los recuerdos — sin contaminarlos jamás. El problema Los MCP de memoria actuales son utilitarios: almacenan y buscan hechos. Eso genera tres fallos conocidos: Confabulación — el agente inventa y lo presenta como recuerdo real. Contexto que no escala — inyectar todo en cada prompt es caro y se desborda. Sin identidad — cambias de modelo o de máquina y el "personaje" desaparece. OurBook cambia el marco: la memoria como identidad narrativa . Lo que lo hace diferente 1. Taxonomía de veracidad (la capa de honestidad) Cada recuerdo tiene un campo veracity : real , observed , imagined (sueño/ficción) o hypothetical . El recall factual excluye por defecto los sueños: un sueño nunca se presenta como hecho. Esto ataca de raíz la confabulación. 2. Soñar = consolidar (neurociencia aplicada) El motor Mnemosyne muestrea fragmentos por saliencia emocional (importancia × valencia) y los recombina en un sueño — como el replay hipocampal durante el sueño. Cada sueño guarda sus fuentes , para que sea ficción honesta y trazable. 3. Doble cerebro: 0 tokens de tu API principal El modelo principal solo pone la voz. Soñar, consolidar y etiquetar corre en Mnemosyne con cadena de fallback: qwen-reverse → local (Ollama/LM Studio) → offline determinista (nunca falla) . Cada llamada queda auditada en engine_log . 4. El libro y la semilla de identidad Todo se exporta a OurBook.md / .html (crón

Somewhere in your codebase there is a line that looks like this: if ( user . plan === ' enterprise ' || user . tenantId === ' acme-corp ' ) { // ... } Nobody remembers who wrote the second half of that condition. It has been there for two years. It is almost certainly still load-bearing. Here is the thing I want to convince you of: that line isn't code. It's data, and it's stored in the worst possible place. Every conditional that encodes a business decision is really a row. It has a condition, an outcome, and a bunch of implicit context about when it applies. You have hundreds of these rows. They're spread across a dozen services, written in four different styles, and there is no way to list them. You have a database. You just can't query it. Five things a database gives you that your code doesn't Once you look at it this way, the problems stop feeling like sloppiness and start feeling structural. There's no schema. One service decides a customer is premium by checking plan === 'premium' . Another checks subscription.tier > 2 . A third checks a flag that was set during a migration in 2023. All three are "the same rule" until the day they aren't, and there's nothing in the system that would notice the drift. There's no way to query it. Try to answer a simple question: what rules are live in production right now? You can't. Someone has to read the source. And grep won't save you, because the interesting conditions are compound, spread across guard clauses, and half of them are expressed as an early return rather than an if . There are no migrations. Changing a rate limit from 100 to 200 requires a pull request, a review, a CI run, and a deploy window. You're pushing a code change through the full pipeline to change a number. It's a schema migration with none of the tooling that makes schema migrations tolerable. There's no audit log. Git tells you who edited the line. It doesn't tell you who decided the rule, when it was supposed to expire, or whether the customer it

I built and open-sourced PacketVoyage —an Agent Skill & MCP server that turns boring traceroute outputs into fascinating stories about physics, geography, and undersea cables. europeanplaice / packetvoyage MCP server & Agent Skill for educational network traceroute analysis, fiber-optic physics verification, and packet voyage storytelling 🚢 PacketVoyage Model Context Protocol (MCP) Server & Agent Skill for educational network traceroute analysis, fiber-optic physics verification, and packet voyage storytelling. Zero external commercial APIs, zero bundled copyright data — pure physical laws and detective insight. 🏛️ Architecture: The Two Pillars PacketVoyage is built around two complementary layers designed specifically for AI-native workflows: ┌────────────────────────────────────────────────────────┐ │ AI Agent (LLM) │ └──────────────┬──────────────────────────┬──────────────┘ │ │ ▼ ▼ ┌──────────────────────────────┐ ┌──────────────────────────────┐ │ 🧠 Agent Skill │ │ 🛠️ MCP Server │ │ (Knowledge / Playbook) │ │ (Capabilities / Execution)│ ├──────────────────────────────┤ ├──────────────────────────────┤ │ • Speed of Light in Fiber │ │ • analyze_voyage_text │ │ (~0.67c, ~10ms / 1,000km) │ │ • voyage_investigate │ │ • Control vs Data Plane math │ │ • run_protocol_experiment │ │ • Disproving GeoIP illusions │ │ • research_host │ │ • Decision Flow & Heuristics │ │ • list_known_iata_airports │ └──────────────────────────────┘ └──────────────────────────────┘ 🛠️ MCP Server (Capabilities & … View on GitHub Ever wondered what’s actually happening behind a trace like this? 1 gateway (192.168.1.1) 0.8 ms 2 * * * 3 ae-1.tokyo-hnd.bb.net (203.0.113.1) 2.1 ms 4 xe-0-0.sjc-core.bb.net (198.51.100.25) 88.5 ms 5 one.one.one.one (1.1.1.1) 88.7 ms Behind these lines lies real-world physics: • The * * * at Hop 2 isn't packet loss: Normal traffic runs at line rate in hardware ASICs (Data Plane), while diagnostic ICMP responses are rate limited by router CPUs (Control Plane). • The +

Seed three events into an audit database, then reach past the application and change one row by hand: $ sqlite3 audit.db "UPDATE audit_log SET principal='attacker' WHERE id=2" $ trelix audit verify --db audit.db Audit chain TAMPERED — first divergent entry id: 2 $ echo $? 1 Delete the newest row instead and it still catches it, naming id 3, even though the surviving rows form a perfectly valid chain. Point it at something SQLite cannot open and it exits 2 rather than 0, because "I could not check" and "I checked and it is clean" must never collapse into the same green build. None of that existed six releases ago. trelix audit verify is one command out of six feature areas that landed in trelix v3.0.0, and it is the one that most changes what the project is for. What the major bump actually is The span from v2.11.0 to v3.1.1 is six releases — v2.11.1, v2.12.0, v3.0.0, v3.0.1, v3.1.0 and v3.1.1, the last of them dated 2026-08-15 — 68 commits, 137 files changed, +19,829/-1,211 lines. v2.11.0 closed out the Jira and Linear connector work, which has its own story. Everything after it is a different kind of release. v3.0.0 carries six new feature areas: Anthropic extended thinking, a model-aware context budget, a VS Code extension that acts instead of merely displaying, a hash-chained append-only audit trail, OIDC SSO, and query-conditioned context compression. Alongside them, an opt-in FTS5 declaration boost for keyword ranking. It is a major bump because of scope, not breakage. Every one of those six is additive and off by default: TRELIX_AUDIT_ENABLED=false , TRELIX_OIDC_ENABLED=false , TRELIX_LLM_THINKING_ENABLED=false , TRELIX_RETRIEVAL_COMPRESSION=false , declaration_boost_enabled False, and context_token_budget still the exact 12_000 integer it was in v2.12.0. A default v3.0.0 install assembles context byte-identically to a default v2.12.0 install, and there is a test that proves it rather than a release note that asserts it. An audit trail you can hand to somebody

GitHub热门项目 | Rust tool to detect cell site simulators on an orbic mobile hotspot | Stars: 5,509 | 21 stars today | 语言: Rust

GitHub热门项目 | Professional Antigravity Account Manager & Switcher. One-click seamless account switching for Antigravity Tools. Built with Tauri v2 + React (Rust).专业的 Antigravity 账号管理与切换工具。为 Antigravity 提供一键无缝账号切换功能。 | Stars: 30,380 | 24 stars today | 语言: Rust

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GitHub热门项目 | The modern link attribution platform. Loved by world-class marketing teams like Framer, Perplexity, Superhuman, Twilio, Buffer and more. | Stars: 24,384 | 25 stars today | 语言: TypeScript

GitHub热门项目 | Open Multi-Agent Interactive Classroom — Get an immersive, multi-agent learning experience in just one click | Stars: 20,766 | 24 stars today | 语言: TypeScript

GitHub热门项目 | Modular SenseNova skills for building AI-powered office assistants and productivity workflows | Stars: 4,916 | 1 star today | 语言: JavaScript

GitHub热门项目 | 🔥 The free & Open Source DocuSign alternative | Stars: 6,841 | 26 stars today | 语言: JavaScript