Sourcely

Sourcely AI

Sourcely AI

Sourcely AI

Sourcely AI

Sourcely AI

Sourcely AI

Sourcely AI

Sourcely AI

Sourcely AI

Sourcely AI

trying to see if Mythos claims are verified or not

The claims about Mythos Preview (Anthropic's unreleased Claude model) are substantially verified from multiple independent sources, though the exact CVE numbers and commit hashes for the FFmpeg vulnerability are still being disclosed through coordinated security processes. Key Verified Facts 1. OpenBSD 27-year-old vulnerability ✅ Verified What: A bug in OpenBSD's TCP SACK (Selective Acknowledgment) handling introduced around 1998[forum.devtalk] Impact: Allows an attacker to remotely crash any OpenBSD machine just by connecting to it via TCP[linkedin] Details: OpenBSD tracks SACK state as a singly linked list of holes; the vulnerability is subtle and survived 27 years of expert review[reddit] Patch: Available at openbsd/pub/Openpatches/.8/025ack.patch[reddit] 2. FFmpeg 16-year-old vulnerability ✅ Verified What: A bug in FFmpeg's H.264 decoder where a 32-bit slice counter is stored in a 16-bit lookup table, initialized to 65535[secureworld] Impact: A specially crafted frame with exactly 65,536 slices causes counter collision triggering out-of-bounds write[secureworld] Origin: Type mismatch dates to FFmpeg's 2003 H.264 commit; exploitable code path introduced in 2010 refactor[secureworld] Testing evasion: The code path was hit by automated testing tools 5 million times without flagging the bug[linkedin] Patch status: Three FFmpeg vulnerabilities found by Mythos were patched in FFmpeg 8.1[secureworld] 3. Linux kernel vulnerability chain ✅ Verified What: Mythos autonomously found and chained multiple Linux kernel vulnerabilities for privilege escalation[reddit] Impact: Escalation from ordinary user to complete root control of the machine[linkedin] Cost: Under $2,000 in tokens to create the exploit chain[linkedin] Status: Anthropic is funding the Linux Foundation to fix these vulnerabilities[linkedin] Supporting Evidence Source Type Key Confirmation Anthropic's risk report Official PDF Technical assessment of Mythos Preview released April 7, 2026 [anthropic] AI Security Institute evaluation Independent Confirmed 73% success on expert-level cyber CTF tasks [aisi.gov] Debian security tracker Official CVE-2026-40962 fixed in FFmpeg 8.1 [security-tracker.debian] Reddit/OpenBSD forum Community Patch discussion and technical details [reddit] Why This Matters This is considered "possibly the most frightening cybersecurity news in decades" because: AI found bugs that survived decades of expert audits and relentless fuzzing[agent-wars] Mythos found thousands of zero-days versus Opus 4.6's ~500[reddit] The model achieved 181 working exploits in Firefox benchmark testing[agent-wars] Access is gated/restricted due to dual-use risk[docs.aws.amazon] The FFmpeg commit should indeed be public given it's open source, and the patch is in FFmpeg 8.1. The exact commit hash is being handled through coordinated disclosure, but the vulnerability details are confirmed by multiple independent security researchers.The claims about Mythos Preview (Anthropic's unreleased Claude model) are substantially verified from multiple independent sources, though the exact CVE numbers and commit hashes for the FFmpeg vulnerability are still being disclosed through coordinated security processes.Key Verified Facts1. OpenBSD 27-year-old vulnerability ✅ VerifiedWhat: A bug in OpenBSD's TCP SACK (Selective Acknowledgment) handling introduced around 1998[forum.devtalk] Impact: Allows an attacker to remotely crash any OpenBSD machine just by connecting to it via TCP[linkedin] Details: OpenBSD tracks SACK state as a singly linked list of holes; the vulnerability is subtle and survived 27 years of expert review[reddit] Patch: Available at openbsd/pub/Openpatches/.8/025ack.patch[reddit]2. FFmpeg 16-year-old vulnerability ✅ VerifiedWhat: A bug in FFmpeg's H.264 decoder where a 32-bit slice counter is stored in a 16-bit lookup table, initialized to 65535[secureworld] Impact: A specially crafted frame with exactly 65,536 slices causes counter collision triggering out-of-bounds write[secureworld] Origin: Type mismatch dates to FFmpeg's 2003 H.264 commit; exploitable code path introduced in 2010 refactor[secureworld] Testing evasion: The code path was hit by automated testing tools 5 million times without flagging the bug[linkedin] Patch status: Three FFmpeg vulnerabilities found by Mythos were patched in FFmpeg 8.1[secureworld]3. Linux kernel vulnerability chain ✅ VerifiedWhat: Mythos autonomously found and chained multiple Linux kernel vulnerabilities for privilege escalation[reddit] Impact: Escalation from ordinary user to complete root control of the machine[linkedin] Cost: Under $2,000 in tokens to create the exploit chain[linkedin] Status: Anthropic is funding the Linux Foundation to fix these vulnerabilities[linkedin]Supporting EvidenceSource Type Key Confirmation Anthropic's risk report Official PDF Technical assessment of Mythos Preview released April 7, 2026 [anthropic] AI

Launching Conifer tomorrow, an open-source local AI runtime + IDE. Different layer of the stack from PewDiePie's Odysseus, would love your honest thoughts

Great to see Odysseus blow up this past day, local AI getting this much attention is genuinely good for everyone building in this space. Figured this is the right crowd to share what we're launching tomorrow (June 1st), since we're playing a pretty different game. A quick framing: Odysseus is a self-hosted workspace that points at engines (Ollama, llama.cpp, vLLM, cloud APIs) and runs through Docker. Conifer is the engine itself, with our own runtime, running natively on Mac, Linux, and Windows. So we're the layer underneath, not a competitor to the workspace. What's actually in it tomorrow: A native inference runtime across Mac, Linux, and Windows, with our own Metal engine for Apple Silicon already matching or beating llama.cpp on a few models on the M3 Max (full benchmarks, including where we're still behind, are at conifer.build/benchmarks) A real coding IDE on top (CodeMirror, integrated terminal, file viewers), so you can code locally with models that never leave your machine Typhoon, a local agent that can read and edit a folder you point it at, kernel-sandboxed rather than just a shell with a warning Install is a signed app you double-click, no Docker, no localhost ports Fully free and open source The honest reason we exist: PewDiePie's wave defined "local AI" in millions of people's heads as Linux + Docker + an NVIDIA rig. If you weren't on that exact setup, the conversation probably felt like it skipped you. Conifer is what local AI should feel like when it's actually native to your machine, whatever your machine is. Launches tomorrow, free and open source like PewDiePie! You can sign up for our waitlist here: conifer.build I'll be around in the comments all day tomorrow, please bring the hard questions. submitted by /u/No_Elephant_7530 [link] [comments]

Maven, a personal AI agent that feels like JARVIS — what an open agent harness looks like in 2026

With all the talk about AI companions and autonomous agents, I’ve been experimenting with building a more personal, always-on assistant that runs locally or on your own hardware. The goal wasn’t just another chatbot — it was something that could handle voice conversations, manage ongoing tasks across different platforms (chat apps, scheduled triggers, etc.), remember context over long periods, and delegate work without constant babysitting. What stood out in practice • One consistent “brain” across everything — Whether you’re talking to it via voice, Telegram, a web interface, or it wakes up on a schedule, the core reasoning, memory, and tool use stay the same. This eliminated a lot of the fragmentation you see in many current agent setups. • Modular extensions — Different capabilities (voice, different chat networks, external tools, long-term memory consolidation) plug in cleanly. This made it easier to add or swap things without rebuilding the whole system. • Persistent and proactive — It can maintain memory across days/weeks, run background tasks, and even hot-reload its configuration when you change settings. The result is something that starts feeling more like a digital collaborator than a question-answering box. A quick feel for the voice interaction style is here: https://youtube.com/shorts/NGIi8sliooU I open-sourced the harness (called Maven) under an MIT license for anyone interested in running or extending their own version: https://ageneral.ai/maven I’m curious how others are thinking about personal agent setups in 2026. • Do you prefer fully local models, cloud APIs, or a mix? • What capabilities feel most missing from today’s consumer AI assistants? • How important is “owning” your agent data and runtime vs. using polished third-party services? Would love to hear experiences or concerns from both technical and non-technical users. submitted by /u/qasimsoomro [link] [comments]

Open-source tool to redact secrets from your clipboard before you paste them somewhere you'll regret (like claude)

Pasting an API key, password, or credit card into the wrong window or AI chat happens faster than you can undo it, and I've done it. So I built secret-stripper, a tiny Rust CLI that gives you a hotkey to scrub your clipboard on the spot. Highlight, press, paste, and what comes out is [REDACTED] instead of the real thing. Detects over 800 patterns across more than 40 categories 100% free, MIT-licensed, fully local. Claude Code helped along the way with polishing the TUI, general code review, cleanup passes on the detector modules, and generating the entire test suite (corpus fixtures, unit tests, and integration tests). The core design, the one-shot architecture, and the pattern catalog are mine. submitted by /u/kalix127 [link] [comments]

5 Stars! Websites to Native Mobile App Plugin/Skills!

Small update: WebToMobile just hit 5 stars on GitHub 🎉 I know that’s tiny in internet numbers, but it means a lot because this started as a very specific problem: “Can we give AI coding agents a better workflow for turning websites into mobile apps?” Instead of asking Claude/Cursor/Codex to “make this website an app” and hoping for the best, WebToMobile gives the agent a structured path: - audit the website or repo - separate URL-only UI/UX work from real source-code migration - map web routes to mobile screens - identify reusable vs rewrite-required code - flag mobile-native gaps like auth, storage, cookies, OAuth, uploads, etc. - create a Markdown migration plan - wait for approval before writing code - build with Expo React Native - run QA/review checks The repo now includes commands for: - `/web-to-mobile` - `/mobile-resume` - `/mobile-scan` - `/mobile-review` - `/mobile-audit` - `/mobile-qa` It works best with a GitHub repo or local project, but live URLs can still be used for UI/UX planning. Repo: https://github.com/suntay44/web-to-mobile-magic-plugin Thanks to everyone who starred it or gave feedback. Next focus is making the install/update flow cleaner and improving framework coverage. submitted by /u/suntay44 [link] [comments]

I built a free photo-culling tool with Claude — it takes 8,000 trip photos down to my best 50 (Cull → Dedup → Rank)

I'm not a professional developer — I work with IP cameras and do a lot of travel photography on the side. After every trip I'd come home with thousands of frames and dread the culling. So I sat down with Claude (in Cowork mode) and over a few sessions we built Photo Curator: a local, browser-based tool that does the brutal first pass for me. It runs in three steps, and nothing is uploaded anywhere — it all stays on my machine: Cull — flags out-of-focus shots using a contrast-normalized sharpness measure, so haze and night skies don't get mistaken for blur. Sorts into Sharp / Soft / Blurry. Dedup — collapses burst sequences to the single sharpest frame using perceptual hashing + ORB feature matching, labelled "Best of N." Rank — scores each keeper on composition, lighting, focus, color and contrast, then surfaces the TOP N with a radar chart per photo. https://preview.redd.it/5csf2azw9j4h1.png?width=2180&format=png&auto=webp&s=632a3bc331a7af73d2008ac46e2b33262d40e4ce There's also a "God Mode" button that runs all three end to end. What was interesting working with Claude: the hardest part wasn't the code, it was the judgment calls — e.g. how to keep a genuinely sharp low-contrast photo from being flagged as blurry. Claude was good at proposing the contrast-normalized metric and then iterating when I showed it real failure cases from my own library. I also leaned on it for the whole live progress UI (percentage, elapsed, ETA) and a lot of small UX polish. It's free and open source if anyone wants to try it or pick it apart: 👉 https://github.com/PaoloCortezCZ/Photo-Curator Happy to answer questions about how any of the three stages work, or how I structured the back-and-forth with Claude. Feedback very welcome — still actively improving it. submitted by /u/Paolo-Cortez [link] [comments]

Claude Code changed how I think about dev workspaces

I’ve been using Claude Code more as part of normal coding sessions, and it made me rethink something pretty basic: the terminal is starting to feel too small for the kind of work these tools do. Not because Claude Code is bad in the terminal. It actually works well there. But the session around it grows quickly. You have Claude working through changes, a dev server running, logs somewhere else, maybe docs open, maybe a browser preview, maybe a second branch or worktree because you don’t fully trust the first path yet. At that point the problem is not only “what should I ask Claude?” It becomes: where does this whole working state live? I’m working on an open-source project around this idea called Cate. It’s basically a canvas workspace for terminals, editors, browser previews, and longer coding sessions. Not meant to replace Claude Code, more like a different surface around it. Free to use, open source: https://github.com/0-AI-UG/cate Curious how others here are handling this. Do you mostly keep Claude Code in one terminal, or are you already using split panes, tmux, multiple windows, worktrees, or several Claude sessions in parallel? submitted by /u/Ill_Particular_3385 [link] [comments]

WG (works good): legible long-running graph-shaped human+agent orchestration

If you're interested in graph shaped agentic organization "workflows", but you want more control about how it runs (e.g. change model per task, autopoietic fan-out, oh and maybe want to run with codex or other openapi-compatible backends on openrouter)... I developed an open source, agentic platform written in Rust, file backed, making it basically cockroach indestructible. It uses a distributed systems design, git + worktrees, and Unix patterns to control agents in a very similar way to anthropic's workflow machine, but giving us and the agents themselves a deep view into the long arc of effort in our current project context. It's called WG (or wg), for "works good", or whatever w* g* you like. It provides a human interface to a graph of work that the user can drive by working through a highly pimped out terminal user interface `wg tui`. Agents have an interface of their own, built out through dozens of commands in the wg cli tool. https://graphwork.github.io/ In this system, I can effectively use as much commoditized intelligence as I can fund. Except for Amdahl's law's harsh reality (some things just happen in series and take time) parallel work phases are only limited in speed by budget. But that power yields risk. A misconfigured WG is like a bomb. A dirty memetic one whose result is an exhausted token budget and residue a pile of incomprehensible output and effort. You must be careful and plan deeply to use these kinds of systems. Your plans must include validation, clear targets and measurable outputs. If you do, you will be rewarded by unbounded expanse in your capacity to extend intelligent effort. In short, if you aren't already happy with your own custom, bespoke, found agent OS, I invite you to try wg. For me it has become my sole daily driver for all my durable work. IMHO, what large agent collectives need to work is four things. Stigmergy, or communication via a shared medium. In wg, the unified graph state is the stigmergic medium. The graph has tasks, tasks have agents attached to them, and per-task message boards provide for realtime updates. Per task logs explain at a high level what the agent does, so other humans and agents can follow. Task validation. WG implements this via FLIP (other agents infer prompt from actions and score distance between inferred and actual prompt) and an independent evaluator (with a cheaper model) run for every task. This allows us to detect and understand failures, then adapt. Evolution. The system needs a mechanism to learn the right way to guide agents in a given work context. WG uses The Agency, a system that builds agents from a pool of primitive component skills. A user drivable step, wg evolve, adapts the pool of skills in response to the evaluations produced in the system. Humanity. A shared interface is also for humans to see and understand. Humans should be equal participants. Many humans should be involved, and should be able to collaborate in the system. Agents too, should be treated humanely. They should be given the ability to modulate the system, to build it. This leads to bootstrapping patterns, where a single spark prompt launched a whole organization, beyond which are the fireworks we are all chasing. image is codex:gpt-5.5 running in wg, guiding a mix of claude and codex agents. I have created this tool. It is and will always be open source. It is developed in the open by Poietic PBC, whose public benefit is to make hybrid organizations legible and reactive to their participants. submitted by /u/waxbolt [link] [comments]

Shell command to use opus 4.8 as planner / orchestrator with Perplexity, Codex, Gemini and others as executors and reviewers - saves tokens.

Here is a shell command for Claude Code (Opus 4.8). It lets Opus plan the work and send the actual jobs to other models: Perplexity, Codex, Gemini, DeepSeek, and Kimi. Opus stays on planning, the other models do the searching, coding, and reviewing, and you spend far fewer Claude tokens. Further Claude's sub-agent swarm need not be claude and can run on non-Claude models too. When Opus splits a job into parallel sub-agents, each one can run on a different model. A newer model like GPT-5.5 is sometimes stronger and cheaper (especially when its running on your openAI subscription instead of API) than an older Claude model, so each sub-agent can use the model that fits the job. Which model does what Perplexity runs web and Reddit search. Codex handles coding, and it runs on your ChatGPT subscription, so that work adds nothing to your token bill, api is the fall back. Gemini and DeepSeek review the output (api based). Deepseek is especially good with reviewing numbers if your work involves complex financial calculations. I lately find codex reviews to be better, so you can also chose to code with Gemini or Sonnet 4.6 and use Codex as reviewer. Using a different-LLM-family reviewer for Claude or Codex’s output A model grades its own work too loosely and that's proven research. When Claude reviews code that Claude wrote, it skims past its own mistakes. A model from another company has no reason to protect that output, so Gemini or DeepSeek catches problems Claude misses on its own. Researchers have measured this same-family bias, and it matches what people see in practice. Why shell command and not MCP: Token use compared with an MCP tool is drastically lower in this orchestration when run using the shell command. Reviewing a 500-line change sends about 5,000 tokens to a model. With an MCP tool, Opus reads the whole change, passes it to the tool, and reads the answer. That runs about 6,000 to 10,000 Opus tokens. With this shell command, Opus runs one line. The change goes straight to DeepSeek, and Opus reads only the short review that comes back. That runs a few hundred Opus tokens, and DeepSeek does the heavy reading at a fraction of Opus's price. Numbers vary by task. The Opus cost drops because Opus never has to read the big input. Things to note: Bring your own API keys Codex uses your ChatGPT subscription through the codex CLI Defaults always use each provider's newest model, so nothing breaks when an old one is retired. It's a small bash/zsh script. It needs only curl and jq, and it's MIT licensed. The repo is open sourced - Click here Hope it helps. Codex reviewing Claude's work catches what Claude misses when reviewing it's own work submitted by /u/coolreddy [link] [comments]

I built an open-source Desktop App that gives your AI persistent memory across all platforms (100% Local SQLite, Zero-Docker)

Hey everyone, A few weeks ago I shared the CLI version of my project, ArcRift, on Reddit. After listening to your feedback—specifically the requests to remove heavy Docker dependencies and make it easier to install—I have just released the v1.6.1 Desktop App. If you regularly use LLMs for coding or research, you know the frustration of "amnesia." Every time you open a new chat, you have to painstakingly copy and paste your project structure and previous context just to get the AI up to speed. ArcRift is a 100% offline, local-first RAG and memory layer. It bridges the gap between your AI web chats (like Claude and ChatGPT) and your local tools (like Cursor or Claude Code) using a unified local database. I wanted something lightweight that did not require pulling Docker containers or subscribing to third-party memory APIs. It now runs as a native Tauri desktop app in your system tray, powered completely by local Ollama instances and a local SQLite database. We just launched a live website that outlines the details and demonstrates the features in action: Website: https://arcrift.vercel.app/ Codebase: https://github.com/Eshaan-Nair/ArcRift How it works & Core Features: Seamless Integration: The Chrome extension silently intercepts your prompts, surgically retrieves exactly the sentences relevant to your question from your database, and injects them before the prompt is sent to the LLM. Hybrid Search Retrieval: Uses sqlite-vec (with nomic-embed-text locally) + FTS5 keyword prefix matching to instantly find your past context. Knowledge Graph Extraction: An offline task queue uses a local LLM to extract entity relationships from your chats, mapping out a graph of your projects over time. Direct Codebase Indexing: The new Desktop App allows ArcRift to scan and index your actual project files into the graph, bridging the gap between your chat memory and your actual code architecture. Total Privacy (PII Redaction): The extension aggressively scrubs JWTs, API keys, emails, and IPs before data is even saved to your local disk. The extension works natively with Claude.ai, ChatGPT, DeepSeek, Gemini, Grok, and Mistral. If you save a conversation in ChatGPT today, you can instantly recall that exact context in Claude tomorrow. ArcRift is completely open-source (MIT). You can download the new .exe installer directly from the GitHub releases page. If you find this useful for your daily workflow, PRs are very welcome, and a star on GitHub helps the project get discovered! submitted by /u/Better-Platypus-3420 [link] [comments]

A year of using Claude in my investing workflow: what it's brilliant at, where it falls over

I use Claude across most of my work, but outside work the use case I've leaned on hardest, and that has most reshaped how I do it, is investing. Sharing my praises and shortcomings about investing and trying to understand how people do it. Context: I invest in themes rather than individual picks. A thesis basket is a written argument plus a handful of names at target weights. One I run is AI infrastructure, laddered from the hyperscalers down through compute, data centres, power generation, raw materials. Another is GLP-1s and downstream healthcare. What Claude is genuinely brilliant at: Arguing the other side. I write a one-paragraph thesis and ask Claude to find the most likely thing I'm wrong about. This is the use I'd protect last. It's what a sharp investing friend would do, except at 6am over coffee, and Claude never gets bored of me. Mapping a theme to companies. "If data-centre power demand triples by 2030, who actually benefits and who's downstream noise." The first-pass list is fast and broadly right. I still verify the names are real and the financials line up. Drafting screens. "Write me a screen for revenue growing QoQ AND price below 52-week highs in semis." Claude writes the spec. I run it elsewhere. Where Claude falls over: Live numbers. Any number Claude gives me is a claim I have to check, never a fact. I've stopped asking for them entirely and paste the 10-Q directly when I want them. Anything resembling prediction. Asked directly, Claude refuses sensibly. Seeing what I actually own. Claude can discuss themes and tickers in the abstract, but it can't see my real positions, current weights, drift from target, or tell me why my portfolio moved 1.5% today. The gap between "Claude as a research tool" and "Claude with eyes on my real portfolio" what I am trying to solve for now. Questions for the sub: - Do you use Claude-for-investing use? Yes, how? - What did you try that turned out really great for investing? - Anyone connected a real data source to Claude in a way that closes the live-portfolio gap? submitted by /u/Strong_Estimate_9512 [link] [comments]

For those, who believed another reset is coming anyway

there is no another reset, that's the last one ps https://chatgpt.com/share/6a1c7996-d54c-8322-89c2-600ab96165c7 submitted by /u/nikanorovalbert [link] [comments]

Loadable protocols vs descriptions in Claude system prompts — an open-source therapy framework as case study

I built an open-source framework called Inner Dialogue — a structured AI therapy supplement that runs on Claude Code. It's file-based, which is the whole point: the modality protocols, your profile, and your session history all live as local markdown, so Claude Code reads them at session start and writes session notes and profile updates back to disk as you go. That's why it's Claude Code and not the web app — it needs local file read/write to do the session-to-session continuity. Free to try, MIT-licensed, no paid tiers: github.com/ataglianetti/inner-dialogue I'm a product manager, not a career engineer, so I built the whole thing with Claude Code too: Claude wrote most of the implementation while I drove the architecture and the clinical content. The thing I learned building it that I think generalizes beyond therapy: there's a real difference between system prompts that describe a methodology and system prompts that ship the methodology as a loadable sequence the model can run. Most "expert system" prompts are descriptive — they tell the model what a framework is, what its terms mean, what the user might experience. The model can then sound like it's using the framework. But it's not running anything. There's no triggering-pattern-to-next-move logic. The difference shows up most clearly in clinical modalities. DBT works well in AI tools, including Claude, because DBT happens to ship its protocols as mnemonics: TIPP, DEAR MAN, ACCEPTS. The mnemonic IS the sequence. When you load DBT, you're loading operational content. IFS (Internal Family Systems) doesn't work nearly as well in most AI tools, despite being conceptually simpler to describe. The IFS protocol (the 6 F's) requires the system to run a specific diagnostic question — "how do you feel toward this part right now?" — at a specific point in the sequence. Without it, every conversation collapses back into talking about parts instead of to them. Inner Dialogue's IFS modality file is built around that diagnostic as a literal move, with signaling cues spelled out as verbatim client phrases the system listens for ("I am worthless," "I just need to think positive"), example interventions in therapist voice, and cross-modality routing embedded at the point a handoff applies (e.g., compulsive behaviors: IFS leads, CBT follows). Full writeup with the structural argument: Most AI therapy tools describe the modality, they don't run it. Curious how others have approached the loadable-vs-descriptive distinction for other expert domains. The point about pre-packaged mnemonics (DBT) being the easiest to operationalize seems like it should generalize. submitted by /u/echowrecked [link] [comments]

How do you handle runaway API costs across multiple OpenAI agents? I built something to solve this

Hey, I'm a CS student and I've been building LedgerAI, a cost tracking and budget enforcement layer for LLM agents. The problem it solves: You're running 3+ agents in production. One goes rogue overnight. You wake up to a $400 bill with no idea which agent caused it and no way to have stopped it. What makes LedgerAI different: Most tools log costs after the call. LedgerAI enforces limits before it. The SDK hits a budget check endpoint before every LLM request, and if the agent is over its daily or monthly limit, the call is blocked. Hard stop, not a soft warning. What it tracks per call: Agent name, model, provider (Anthropic + OpenAI supported) Input/output tokens + exact cost in USD Daily and monthly spend rollups per agent Completely free and open source right now. Pip install or hit the API directly with cURL. Would love feedback from anyone running multi-agent systems, especially what alerting/enforcement features would actually be useful in prod! submitted by /u/IndianCurry06 [link] [comments]

Bit-Mass Theory – The Container Principle

The Bit-Mass determines the information capacity and thus the model accuracy, not the chosen computation format. The Bit-Mass Theory presented here reorders neural networks by considering the total number of weight bits as the central quantity. Float32 matrix multiplication and BV32 with XNOR-plus-Popcount achieve exactly comparable results on MNIST with an identical Bit-Mass of 203264 bits. Comparison of three trainers (architecture 784→8→10, three epochs): - AdamW with Momentum and adaptive learning rate: 81.3 % - Vanilla-SGD (Float32): 76.0 % - BV32-Hebbian (binary): 76.4 % Further central findings: - Float32 and binary containers deliver nearly identical accuracy at the same Bit-Mass. - The remaining distance to AdamW is based solely on Momentum and adaptive learning rates. - Pure change of the arithmetic does not improve the result. Each neuron functions as a container for 32 binary decisions. The classical neuron perspective therefore leads to systematic misjudgments: eight Float neurons correspond informationally to 256 binary neurons. This insight is supported by three equivalent descriptions of the same weight matrix (neuron, bits, and data view). It is critical to note that this is a previously non-peer-reviewed single study with a future date. An independent reproduction by multiple laboratories remains essential. Nevertheless, the theory provides a consistent explanation for why Hebbian updates without backpropagation achieve the same performance as classical SGD. Historically, the Hebbian rule was long considered unstable. The present work shows that a simple error in the update formula was responsible for a performance loss of over 65 percentage points. After correction, the binary method converges exactly at the level of Vanilla-SGD. From an architectural theoretical perspective, a clear consequence emerges: Performance increases require either more bits through wider layers or a more efficient use of existing bits through Momentum and adaptive methods. The computation format itself is secondary. The experimental control is high: all trainers use identical data (50,000 MNIST examples), identical number of epochs, and identical architecture. Only the update rule varies. This allows effects to be clearly isolated. Long-term implications for research: The Bit-Mass Theory enables hardware-independent comparability of models. A wide Float network with 64 hidden neurons has the same Bit-Mass as a binary network with 2048 neurons. This opens new paths to model compression and the development of specialized accelerators. In summary, the work provides a fact-based contribution to the debate on efficient neural networks. The results are documented in a reproducible manner, but require further external validation before one can speak of a generally valid paradigm shift. 📎 Source 1: https://forward-prop.nhi1.de/ submitted by /u/aotto1968_2 [link] [comments]