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How Clioloop's Agentic Fusion Works: A Technical Deep Dive

Architecture Overview Clioloop's Agentic Fusion is not just "run the same prompt 5 times and pick the best." It's a structured pipeline where different models play different roles, with strict security boundaries between them. The Pipeline Step 1: Planning Phase When you run /fusion , up to 5 planner models are dispatched in parallel. Each planner: Receives your original prompt and context Has read-only tool access — they can search the web, read files, but never modify anything Proposes an approach (not an answer — an approach) The planners might suggest different strategies: Planner A: "Search the web for similar problems, then write a script" Planner B: "Read the existing codebase first, then modify in place" Planner C: "Break it into subtasks and use the Kanban system" Step 2: Execution Phase Your main model takes the planners' proposals and does the actual work: Full tool access (file editing, shell, web, browser, image gen, etc.) Fully visible — you watch every file edit, every command, every web search in real time Not a black box — you can intervene at any time This is the key difference from "ensemble" approaches: the main model does real work with real tools, not just text generation. Step 3: Review Phase Up to 5 reviewer models critique the draft: Read-only access — they can see what the main model produced, including generated images They check for errors, suggest improvements, flag problems Each reviewer works independently Step 4: Verdict Loop The draft is revised based on reviewer feedback: If reviewers find issues, the main model gets the feedback and revises The loop continues until reviewers approve You get the final, reviewed answer Step 5: Fusion Everything combines into one answer that has already passed independent review. Security Model The safety comes from schema-level restrictions : Role Can Read Can Write Can Execute Planners ✅ Files, web, images ❌ Nothing ❌ Nothing Main Model ✅ Everything ✅ Files ✅ Commands Reviewers ✅ Draft, files, image

2026-06-19 原文 →
AI 资讯

Clioloop: The Open-Source AI Agent That Thinks in Teams

The Problem Most AI assistants give you one model's answer. If it's wrong, you catch it or you don't. If you use a cheap model, quality drops. If you use a frontier model, you pay frontier prices for everything — even a simple file rename. What is Agentic Fusion? When you run /fusion , a panel of models collaborates on your task: Planners (up to 5): Read-only models that research and propose routes in parallel. They figure out the best approach but can't touch your files or run commands. Main model : Your chosen model does the actual work — full tool access, fully visible. You watch every step. Not a black box. Reviewers (up to 5): Read-only models that critique the draft. They can see images the main model generated. They check for errors, suggest fixes, flag issues. Verdict loop : The draft is revised until reviewers approve. The answer you get has already passed independent review. Fusion : Everything combines into one reviewed, approved answer. The quality comes from synthesis — not from running the same job 5 times. Cheap open models combine into something that rivals a frontier model at a fraction of the cost. Safety by Construction Planners and reviewers are read-only at the schema level. They can research and critique, but they can never touch your files or execute commands. Only your main model has tool access, and you watch it work live. Beyond Fusion Clioloop is also: Self-improving : Keeps MEMORY.md and USER.md , updated automatically Autonomous : Set a standing goal with /goal and it loops until done Everywhere : Terminal, desktop app, web dashboard, Telegram, Slack, Discord, WhatsApp Multi-agent Kanban : Break big work into tasks with worker agents Tools : File editing, shell, web search, browser, image/video gen, TTS, MCP Scheduled jobs : Run on cron for automated workflows Open-source : Self-host everything, own your data The Omni Loop Portal One OAuth login gives you access to 300+ models. No API keys. An OpenAI-compatible proxy means any tool works

2026-06-19 原文 →
AI 资讯

Quill vs spdlog: Which C++ Logger Is Better for Low-Latency Applications?

Logging has a habit of ending up in the places you care about most. It starts as a few lines for visibility. Then those lines appear in request handling, market-data processing, matching loops, telemetry pipelines, and other code where predictable latency matters. At that point, a log statement is no longer just observability. It is work running on the same thread you are trying to keep fast. A line like this can look harmless: LOG_INFO ( logger , "order_id={} price={}" , order_id , price ); The important question is what happens before the caller continues . Does it evaluate expensive arguments? Format text? Copy buffers? Allocate? Contend with other producer threads? Wait for queue space? For many applications, those costs are acceptable. For latency-sensitive systems, they are part of the latency budget . spdlog is one of the best-known C++ logging libraries and a strong general-purpose choice. It is mature, easy to use, and has a broad feature set. Quill was designed for a narrower problem: How little work can a C++ logger leave on the caller thread while still producing rich, human-readable logs? That is the lens for this comparison. The interesting difference is not which library has more features. It is where each library chooses to spend work. At a Glance Area spdlog async Quill User-message formatting Producer thread Backend thread Producer handoff Shared thread-pool queue Per-thread SPSC queue Arguments for runtime-disabled levels Evaluated if the level was not compiled out Skipped by the macro-level runtime check Native synchronous mode Yes No Backend workers Configurable thread pool Single backend worker Primary focus General-purpose flexibility Low producer-side latency These differences do not make one library universally better. They make each library better suited to different workloads. Async Logging Is Not One Design "Async logging" often means "file I/O happens on another thread." That is useful, but it is not enough to describe the cost paid by t

2026-06-19 原文 →
AI 资讯

Perl PAGI Project Updates

Quick update to anyone interested in upcoming changes to the PAGI project (spiritual successor to Plack/PSGI). 1) Distribution split up: when we released PAGI, we initially released everything as one distribution. PAGI ( https://metacpan.org/pod/PAGI ) currently has a) the PAGI specification; b) the reference server and c) a bunch of ease of use tools, similar to the role that the Plack distribution played for PSGI. Putting everything into one place was just to make my life easier as in the early bunch of releases there was a lot of fixes and updates, most of which cut across all three parts of PAGI. Also I wanted to make it easy for people getting into PAGI to be able to explore the ecosystem. However now that code seems to be settling down having these in independent repos and releases makes more sense. Going forward the PAGI repo will only update if the spec itself changes; PAGI::Server and PAGI::Tools (where all the utilities and helpers now go) likewise. I think this will start to bring some stability to the ecosystem, especially now that PAGI::Server is functionally complete based on the goal chart I had for it initially. So I will only update it to fix bugs and security issues. PAGI::Tools will probably continue to see evolution over the summer as I start to nail down more common use cases and identify patterns worth encapsulating. 2) Specification clarifications and updates: The PAGI specification itself will move to v0.3 in the next release and it contains mostly clarifications and fixes. Biggest change will be a more detailed mechanism for controlling streaming output, especially around handling back pressure as well as new callbacks to notice when the output buffer is getting full and when it clears. Hopefully these changes will make it easier and more reliable to do streaming in PAGI. PAGI::Server has been updated to match, and the response helper in PAGI::Tools has some updates around that as well. Currently all this sits on Github: https://github.com/j

2026-06-18 原文 →
AI 资讯

I built Proofline because AI agents are getting too good at sounding finished

AI agents are getting very good at writing final reports. The problem is not only that they make mistakes. The problem is that sometimes they make mistakes with excellent presentation. Proofline is a 5-skill Markdown pack that catches fake-ready output before it turns into a release, handoff, public post, or "yeah, looks done". What Proofline does It is not trying to be another giant agent. It works as a review route after the agent produces a result: Reference Gap Ready Gate Reality QA Lean Pass Repair Report Compiler Each step asks an annoying but useful question: what is missing from the references, what was not checked, where did the agent pretend everything was fine, and what actually needs to be fixed? Who it is for Builders working with Codex-style agent chats, AI coding workflows, Markdown handoffs, and any process where "done" needs to mean more than a confident paragraph. Release: https://github.com/aisflows/proofline/releases/tag/v0.2.0-rc5

2026-06-18 原文 →
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