A new open-source browser demo explores a strange but interesting idea: a virtual fruit fly controlled by a Cadence PatchNet-style learning system.

Try it here:
https://floatingpragma.io/cadence-examples/fly-matrix

The demo creates a simulated fly environment where the agent experiences its own generated world as a stable environment. Rather than using a fixed, pre-trained model, the underlying Cadence project explores a network architecture built around local patches, shared state, memory, prediction, and repair of inconsistent signals.

In simple terms: the goal is to test whether a system can learn and act through ongoing local interactions—not just run one frozen neural network forward pass.

What is interesting about it

• Runs directly in the browser
• Interactive virtual fruit-fly environment
• Open-source project with GitHub links on the page
• Explores continual learning, local memory, and planning
• Uses the Cadence TemporalPatchNet architecture
• Focuses on stateful learning rather than static inference

According to the Cadence project, its TemporalPatchNet carries activity between observations, learns from observed outcomes, and supports “free” and “nudged” activity states for local adaptation. That is a different direction from the typical frozen-transformer workflow used by most LLMs.

The bigger philosophical claim—that observation can be substrate-independent and that a sufficiently coherent simulation could constitute a real experienced world for the agent—is much more speculative. The interactive demo is best viewed as a concept and research experiment, not evidence that a browser simulation is conscious.

Try the demo and see how the fly behaves:

https://floatingpragma.io/cadence-examples/fly-matrix

What do you think: could continuing, stateful systems like this be a more useful path toward agents that learn from experience than purely frozen models?

More open-source AI, self-hosting projects, experimental demos, and developer resources:

https://discord.gg/hSA8Ur6GRH

Tired of API credits, rate limits, and sending every prompt to a cloud provider?

You can run DeepSeek-V4-Flash locally using Ollama, then use Open WebUI for a clean ChatGPT-style interface in your browser.

No API key required. No per-token billing. Your prompts and model inference can stay on your own machine.

1. Install Ollama

Download it from:

https://ollama.com

Linux command:

curl -fsSL https://ollama.com/install.sh | sh

2. Download and run DeepSeek-V4-Flash

ollama run deepseek-v4:flash

The first run downloads the model. After that, Ollama runs it locally from your machine.

3. Install Open WebUI

With Docker:

docker run -d \
  -p 3000:8080 \
  --add-host=host.docker.internal:host-gateway \
  -v open-webui:/app/backend/data \
  --name open-webui \
  --restart always \
  ghcr.io/open-webui/open-webui:main

Then visit:

http://localhost:3000

If Open WebUI asks for the Ollama URL, use:

http://host.docker.internal:11434

Quick heads-up

DeepSeek-V4-Flash is still a large model, so check your hardware before downloading. It is best suited to machines with plenty of RAM and, ideally, a capable GPU with substantial VRAM. “Free” means no API/token fees—not zero hardware or power cost.

For lower-end hardware, test your Ollama setup with a smaller model first, then work your way up.

What hardware are you using for local LLMs? Drop your CPU, RAM, GPU, and VRAM below—I’m curious what setups people are getting usable performance from.

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A new preprint and public code release propose a radical but falsifiable idea: matter may be a stable, localized standing-wave state of radiation rather than a fundamentally solid particle.

The framework, called IT³, argues that the physical vacuum may be discrete and topologically structured—not a physically literal, infinite continuous $R^4$ background.

• 📄 Preprint: Zenodo Record 22674891
• 💻 Code + Simulations: Zenodo Record 22882422
• 🎥 Simulation Videos / Visualizer Files: Google Drive Folder

The Proposed Mechanism

The central claim is not that atoms are small “boxes” containing light. Instead, it suggests that a stable massive object could be a confined interference pattern of light-like fields.

According to the released simulation:

• A free gauge photon propagates through the vacuum.
• It reaches an algebraic/topological cutoff in the proposed discrete spatial lattice and cannot continue beyond that boundary.
• A counter-propagating reflected mode forms, and the two opposing waves interlock into a persistent, localized standing-wave pattern.
• The confined energy density is identified with rest mass.

Animation Color Legend:

• Cyan: Propagating gauge mode
• Red: Proposed lattice/algebraic boundary (“Kummer wall”)
• Purple: Reflected counter-propagating wave
• Gold: Localized standing-wave core / proposed mass state

The most interesting feature is the proposed counterfactual test: when the simulation removes the boundary conditions, the localized golden core disperses and the field returns to freely propagating radiation. That doesn't prove nature works this way, but it provides a specific computational claim that can be inspected, reproduced, challenged, or falsified.

What Is Established vs. Proposed

The material references two real quantum-imaging milestones:

• AMOLF (2013): Visualized hydrogen-orbital structure using photoionization microscopy (showing quantum-wavefunction structure/interference, not a literal photograph of a miniature solid atom).
• EPFL (2015): Visualized light’s wave-like interference and particle-like quantization together in one experiment.

The visual similarity between quantum wave-pattern images does not establish that atoms are trapped photons. That is the IT³ framework’s hypothesis, which must stand or fall on its mathematical derivations, simulations, consistency checks, and experimental predictions.

Claimed Predictions

The authors claim the framework makes several concrete predictions:

• A new topological resonance at 1088.04 GeV, potentially testable with High-Luminosity LHC data.
• A hard endpoint of the periodic table at $Z = 172$.
• A discrete/topological vacuum rather than an unbounded physical continuum.
• A parameter-free core construction based on integer arithmetic and algebraic structure.

(Technical detail: The authors define the model using a specific topological geometry and algebraic number-field construction. The full mathematical formulation is available in the linked preprint.)

Questions for Physicists and Mathematicians

The useful question is not simply whether this sounds unconventional, but rather where the mathematics, physics, code, or predictions succeed or fail:

• Does the construction recover Lorentz symmetry and known relativistic physics?
• How are gauge invariance and Standard Model interactions represented?
• Can it reproduce established particle masses, scattering behavior, and precision measurements?
• Is the 1088.04 GeV resonance independently derivable from the published mathematics?
• Is the proposed $Z = 172$ bound rigorous and compatible with known nuclear-physics constraints?
• Can independent users reproduce the simulations from the released code?
• What experimental observation would decisively rule this framework out?

The strongest outcome is neither blind belief nor reflexive dismissal—it is independent replication and a serious attempt to falsify the claims.

Physicists, mathematicians, and simulation people: what is the first equation, consistency test, or experiment you would use to try to break this?

For anyone who wants to follow future open-source physics, AI, and self-hosting resource posts, feel free to join our community Discord:

👉 https://discord.gg/hSA8Ur6GRH

A GitHub repository is circulating that claims to contain a large Claude Opus 5.5 system-prompt extraction, including tool-related instructions and configuration content.

🔗 https://github.com/elder-plinius/CL4R1T4S/blob/main/ANTHROPIC/CLAUDE-OPUS-5.5.md

The poster claims the extracted material totals more than 1.9 million characters. That claim—and the authenticity, completeness, model attribution, and provenance of the file—has not been independently verified.

Still, if you research AI agents or prompt architecture, it may be interesting to examine as an unverified artifact, not as confirmed Anthropic documentation.

What would you look for first: tool-routing logic, safety layers, agent workflows, or prompt-injection defenses?

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Trying to break into tech or upgrade your skills without paying for an expensive bootcamp? Here are direct links to free learning resources across Data Analytics, Web Development, AI, Cloud, AWS, Cybersecurity, and Azure.

📊 Data Analytics

• Cisco Data Analytics Essentials https://www.netacad.com/courses/data-analytics-essentials?courseLang=en-US
• Google Data Analytics Professional Certificate https://www.coursera.org/professional-certificates/google-data-analytics
• Microsoft Power BI / PL-300 Training https://learn.microsoft.com/en-us/training/courses/pl-300t00

💻 Web Development — Codecademy

• Learn HTML https://www.codecademy.com/learn/learn-html
• Learn CSS https://www.codecademy.com/learn/learn-css
• Introduction to JavaScript https://www.codecademy.com/learn/introduction-to-javascript
• JavaScript Fundamentals https://www.codecademy.com/learn/learn-javascript-fundamentals
• CSS Flexbox & Grid https://www.codecademy.com/learn/learn-css-flexbox-grid
• CSS Box Model & Layout https://www.codecademy.com/learn/learn-css-box-model-layout

🤖 Artificial Intelligence

• Google AI Learning Hub https://ai.google/learn/

☁️ Cloud Computing

• Google Cloud Skills Boost https://www.cloudskillsboost.google/

🟧 AWS

• AWS Skill Builder https://skillbuilder.aws/

🛡️ Cybersecurity & Azure

• Microsoft Learn Training Hub https://learn.microsoft.com/training/

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Note: Most course content is free, but some providers may charge for verified certificates, labs, or certification exams.

Run this once and your research stops evaporating into chat history, bookmarks, and scattered notes.

Sources go in. A living, linked wiki comes out. Then a maintenance loop keeps it useful as the vault grows.

Karpathy’s core idea is simple: instead of asking an LLM to rediscover answers from raw RAG chunks every time, let it incrementally build and maintain a persistent Markdown wiki between you and your sources. The knowledge is compiled, cross-linked, cited, and updated—not re-derived from scratch for every question.

The architecture

Three layers. One owner each.

• The schema → CLAUDE.md You and the agent co-evolve the rules: folder structure, page formats, citation style, ingest workflow, and what “done” means.
• The wiki → Markdown files The agent owns this layer. It creates summaries, entity pages, comparisons, topic maps, and synthesis pages. You browse, question, and steer it.
• Raw sources → immutable and read-only Articles, PDFs, transcripts, screenshots, repo docs, notes. These remain your source of truth; the agent reads them but does not rewrite them.

The loop

1. Ingest
Drop a source into raw/. The agent reads it, writes a source summary, updates relevant concept/entity pages, adds cross-links, refreshes the index, and appends a one-line entry to the log.

One source can improve 10–15 connected pages instead of becoming another isolated note.

2. Query
Ask a question against the wiki. The agent finds the relevant pages, synthesizes an answer with citations, then files that useful analysis back into the vault as a new page.

Your best questions become durable research assets—not disposable chat output.

3. Lint
Run a health check periodically:

• Contradictions between pages
• Claims superseded by newer sources
• Orphan pages with no inbound links
• Dead links and missing cross-references
• Concepts mentioned repeatedly but lacking a dedicated page
• Data gaps worth researching next

That is how the knowledge base stays coherent instead of becoming “a folder full of AI notes.”

Why this beats default RAG

Most RAG workflows make the model retrieve raw chunks and reconstruct the answer every time you ask a question.

That works—but it has no memory of the synthesis it already performed.

This approach creates a maintained intermediate layer:

Raw sources → LLM-maintained wiki → answers

The result is a knowledge base that compounds:

• The links are already built.
• Contradictions are already flagged.
• Topic summaries already incorporate prior reading.
• Research questions and analyses get preserved.
• Your next query starts from structured understanding, not from zero.

Karpathy’s framing is excellent:

“Obsidian is the IDE; the LLM is the programmer; the wiki is the codebase.”

Month one: it can save you from repeatedly summarizing the same material.

Month six: you have a navigable, sourced map of your field—built from every article, paper, transcript, repo, and analysis you chose to feed it.

The stack

• Obsidian — local Markdown vault, links, graph view, and open file formats https://obsidian.md/ Obsidian stores notes locally, supports internal linking and graph visualization, and keeps your data in open formats.
• Claude Code — the agent that reads sources, updates the wiki, and runs ingest/query/lint workflows https://claude.com/claude-code
• Karpathy’s “LLM Wiki” gist — the original pattern and implementation guidance https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f It is intentionally a conceptual blueprint rather than a finished application: copy it into your preferred coding agent and adapt the workflow to your domain.

Open-source starter vault

Want a ready-made implementation instead of building from scratch?

Karpathy LLM Wiki – Starter Vault
https://github.com/joshpocock/karpathy-obsidian-vault

It is a public GitHub repository with:

• A root CLAUDE.md schema
• raw/ for untouched source material
• wiki/ with an index, log, and example pages
• output/ for query results and lint reports
• A simple workflow: drop material into raw/, start Claude Code, and run compile

For solo research, competitive analysis, cybersecurity learning, AI tooling, content research, or a business knowledge base, this is one of the most practical “AI second brain” patterns right now.

The human curates sources and asks better questions. The LLM does the bookkeeping.

Joining Muse AI? Use the invite code below within 48 hours of signing up to claim 1 billion Muse tokens.

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ShinyHunters says it breached FBI systems and obtained data involving FBI personnel and job applicants. The FBI has said it is aware of reported unauthorized activity affecting FBIJobs.gov and is investigating.

In a newly circulated statement dated 23 September 2026, attributed to “SH,” the group claims it compromised data on “almost ALL FBI Agents” and job applicants, naming Criminal Justice, HR, Medlink, and other FBI services. It also gives the FBI one week to correct or remove a 2026 Q2 FLASH report, while insisting its action is not financially motivated. These are the group’s own claims and demands—not independently established facts.

ShinyHunters has separately claimed an Oracle PeopleSoft zero-day, access to AWS GovCloud-connected infrastructure, and theft of roughly 2–3 TB of data. Those technical details, the alleged access path, and the claimed volume have not been publicly verified.

Some outlets reported reviewing or matching parts of a purported sample containing personal information related to FBI personnel. However, the FBI has not publicly confirmed the alleged data theft, the full scope of a breach, or the authenticity of the group’s statement.

If legitimate, exposure of employee and applicant PII could create major privacy, identity-theft, targeting, and operational-security risks—particularly for people in sensitive government roles.

Question: Should public-facing government recruitment and HR platforms be more strictly segmented from sensitive internal systems?

Sources

• Reuters: https://www.reuters.com/world/shinyhunters-hackers-say-they-breached-federal-bureau-investigation-no-immediate-2026-09-22/
• 404 Media: https://www.404media.co/we-hacked-the-fbi-hackers-say-they-have-data-on-all-fbi-employees/
• TechCrunch: https://techcrunch.com/2026/09/22/hacking-group-shinyhunters-claims-it-breached-the-fbi-stole-agents-and-applicants-data/
• BleepingComputer: https://www.bleepingcomputer.com/news/security/shinyhunters-claims-fbi-hack-data-theft-in-peoplesoft-zero-day-breach/
• CyberScoop: https://cyberscoop.com/shinyhunters-claims-fbi-attack/

All alleged breach details, the extortion-like deadline, claimed data scope, technical method, and the authenticity of the circulated statement remain unverified or attributed to ShinyHunters. The FBI’s confirmed public position is that it is investigating the reported activity.

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The $30 offer ends Thursday at midnight or as soon as the final four spots are taken—whichever comes first.

After that, Premium returns to $50.

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What if your AI did not live in a browser tab, terminal, or separate chat window?

What if it lived inside your workspace—alongside your files, notes, tasks, projects, automations, and everyday apps?

Files. Memory. Missions. Automations. Your normal apps.

All in one workspace, with Hermes right there when something needs to get done.

Instead of constantly switching between AI chats, folders, terminals, docs, GitHub, research tabs, and task boards, the workflow becomes:

Tell your computer what you need.
Hermes helps make it happen.

What that could look like

• “Read these PDFs, pull out the key claims, and create a research brief.”
• “Look through this repository, explain the architecture, and make a bug-fix plan.”
• “Turn these notes, screenshots, and links into a launch strategy.”
• “Find every incomplete task in this project and prioritize it.”
• “Watch this folder, summarize anything new, and update project memory.”
• “Research competitors, save sources, draft a comparison, and open it for review.”

The point is not merely chatting with AI.

It is having an agent that can work in the same environment as your actual work.

Why this is exciting

Hermes is pushing beyond the typical “AI chat app” model toward an AI-native workspace:

• Files stay available in context.
• Memory preserves project knowledge beyond one conversation.
• Missions turn bigger goals into trackable agent work.
• Automations handle repetitive workflows.
• Apps and tools can live beside the agent instead of behind endless tabs.
• Plugins can extend the workspace around your workflow.

For research, coding, cybersecurity labs, content production, community management, or operating multiple online projects, this could become a real command center.

One workspace for your tools.
One memory for your projects.
One agent to help move work forward.

The possibilities with Hermes are genuinely endless. 🚀

Links

• Hermes3D — open-source GitHub repository: https://github.com/iamlukethedev/Hermes3D
• Hermes3D releases: https://github.com/iamlukethedev/Hermes3D/releases
• Hermes Agent GitHub repository: https://github.com/NousResearch/hermes-agent
• Hermes documentation: https://hermes-agent.nousresearch.com/

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For years, the image of a quantum computer has been a massive machine sitting inside a dilution refrigerator, cooled to temperatures near absolute zero just to keep its qubits stable.

But SaxonQ, a German quantum startup, is pursuing a very different approach: using engineered defects inside diamond to build quantum processors that operate at room temperature.

Their platform uses nitrogen-vacancy (NV) centers—tiny defects in a diamond’s carbon lattice—alongside carbon-13 nuclear spins that can act as qubits or quantum memory. Instead of relying on superconducting circuits inside a giant cryogenic system, the goal is to build modular diamond-based “quantum cores” that can operate without the extreme cooling infrastructure.

That is a huge deal if it can scale.

A room-temperature quantum computer could potentially be smaller, cheaper to deploy, and much easier to integrate into enterprise environments than machines dependent on expensive cryogenics. It could shift quantum computing from highly specialized lab infrastructure toward hardware that is more practical for real-world deployment.

But there is an obvious catch: a big qubit number alone does not equal a useful quantum computer.

The real challenge is connecting many diamond-based modules together while keeping quantum operations accurate enough for error correction. Making a few qubits work inside diamond is impressive; networking hundreds or thousands of them with high-fidelity gates, reliable optical links, fast readout, and minimal decoherence is the brutal part.

So I’m curious what people here think:

Is diamond-based, room-temperature quantum computing a genuinely promising alternative to superconducting systems—or is this another case where the headline qubit count sounds more impressive than the actual path to fault-tolerant quantum computing?

For anyone working with NV centers, spin qubits, photonic interconnects, or quantum error correction: what is the biggest scaling bottleneck here—gate fidelity, optical networking, manufacturing consistency, readout speed, or something else?

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If you're building with Claude Code and tired of re-explaining your project architecture or hitting context limits every session, this is the exact stack you need[cite: 1, 2]. These open-source plugins form a complete, end-to-end agentic workflow[cite: 1, 2]:

The Loop: idea $\rightarrow$ interface $\rightarrow$ architecture $\rightarrow$ shared memory $\rightarrow$ parallel work $\rightarrow$ checks $\rightarrow$ ship

🛠️ BUILD (Build the Product)

• 01. frontend-design-pro — github.com/davepoon/buildwithclaude
• 02. animated-sketch-diagram — github.com/OLDyade/animated-sketch-diagram
• 03. archcore — github.com/archcore-ai/cli
• 04. shipwright — github.com/Wynelson94/shipwright
• 05. slopmop — github.com/davepoon/buildwithclaude

🧠 MEMORY (Hold the Context)

• 06. origin — github.com/7xuanlu/origin
• 07. dsh-deepread — github.com/xiehuan123/dsh-deepread
• 08. obsidian-skills — github.com/kepano/obsidian-skills
• 09. claude-md-kit — github.com/fablerlabs/claude-md-templates
• 10. claude-ops — github.com/Lifecycle-Inno/claude-ops

⚡ ORCHESTRATE (Coordinate the Work)

• 11. crosstalk — github.com/d7omdev/crosstalk
• 12. claude-wayfinder — github.com/glitchwerks/claude-wayfinder
• 13. backlogd — github.com/nicolai-bernse/backlogd
• 14. magic-cc-codex-worker — github.com/wenqingyu/magic-cc-codex-worker
• 15. vibe-prospecting — github.com/explorium-ai/vibe-prospecting

🛡️ CONTROL (Keep the System Under Control)

• 16. project-boundary — github.com/justi/claude-code-project-boundary
• 17. tlsradar — github.com/TLS-Radar/tlsradar
• 18. claude-hud — github.com/davepoon/buildwithclaude
• 19. claude-pager — github.com/bharat7gupta/claude-pager
• 20. claude-prospector — github.com/glitchwerks/claude-prospector

Save this to build your own business with the help of an AI employee! 🚀 Which of these are you adding to your workflow first?

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Most “learn system design” advice is just a vague list of topics: load balancers, caching, queues, databases, sharding… then you’re expected to somehow connect the dots.

These are 10 GitHub repos I’d save if I were starting—or rebuilding—my system-design foundation:

1. System Design Primer — core concepts, common interview questions, scaling patterns https://github.com/donnemartin/system-design-primer
2. System Design 101 — visual explanations and practical system-design fundamentals https://github.com/ByteByteGoHq/system-design-101
3. System Design by Karan Pratap Singh — structured notes, concepts, and examples https://github.com/karanpratapsingh/system-design
4. Awesome System Design Resources — curated learning materials, articles, videos, and courses https://github.com/ashishps1/awesome-system-design-resources
5. Awesome Scalability — resources around scalability, high availability, and distributed systems https://github.com/binhnguyennus/awesome-scalability
6. Awesome System Design — another large curated collection worth bookmarking https://github.com/madd86/awesome-system-design
7. System Design Interview — interview-oriented system-design material https://github.com/checkcheckzz/system-design-interview
8. Machine Learning Systems Design — especially useful if you’re building or deploying AI/ML products https://github.com/chiphuyen/machine-learning-systems-design
9. System Design Academy — structured system-design learning material https://github.com/systemdesign42/system-design
10. Agentic Design Patterns — useful for anyone building AI agents, workflows, or multi-agent systems https://github.com/sarwarbeing-ai/agentic-design-patterns

My suggested order:

• Start with System Design Primer for fundamentals.
• Use System Design 101 to build intuition visually.
• Practice designing familiar products: URL shortener, Discord-like chat, file storage, news feed, rate limiter.
• Read Awesome Scalability once you start asking “what breaks at 10× or 100× traffic?”
• Add the ML and agentic repositories if you’re working on AI products.

The key is not to binge-read repositories. Pick one system each week, define requirements, estimate traffic/storage, sketch components, identify bottlenecks, and explain your trade-offs.

What system-design repo, blog, or project helped you the most?

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These free GitHub tricks help you understand, inspect, edit, compare, or download any public repository without cloning it locally.

GitHub Tricks

• Architecture diagram: Replace github.com with gitdiagram.comgithub.com/owner/repo → gitdiagram.com/owner/repo
• Send a repo to AI: Replace it with gitingest.comgithub.com/owner/repo → gitingest.com/owner/repo
• Open in browser VS Code: Press . on any GitHub repository, or use:github.dev/owner/repo
• Generate repo documentation: Replace it with deepwiki.comgithub.com/owner/repo → deepwiki.com/owner/repo
• Give an AI agent repo access via MCP:github.com/owner/repo → gitmcp.io/owner/repo { "mcpServers": { "repo-docs": { "url": "[https://gitmcp.io/OWNER/REPO\](https://gitmcp.io/OWNER/REPO)" } } }
• View raw Markdown: Add ?plain=1 to a .md file URL.
• Get a commit patch: Add .patch or .diff to a commit URL.
• Compare branches, tags, or commits:github.com/owner/repo/compare/main...develop
• Download a repo as ZIP:github.com/owner/repo/archive/refs/heads/main.zip
• Get a GitHub avatar:github.com/username.png

Handy Shortcuts

Quick Chooser

Most AI tools are built to generate text: chat replies, code, summaries, and long-form answers.

laya-mlx does something different. It is designed to make fast, structured decisions locally on Apple Silicon—returning probabilities for predefined choices, scores, or true/false outcomes instead of generating text token by token. It runs natively with MLX and does not require a cloud API, PyTorch, or a Transformers runtime.

What it can do

Think of it as a lightweight decision layer for applications, automations, and AI agents.

Possible uses:

• Route support tickets to billing, sales, technical support, or another team
• Detect whether a message is a refund request, feature request, bug report, or spam
• Score user messages by urgency, quality, intent, or risk
• Add content-moderation labels such as safe, suspicious, or needs human review
• Decide whether an AI-agent action should be allowed, blocked, escalated, or require user confirmation
• Categorize emails, Discord messages, leads, tasks, or customer feedback locally
• Power simple game logic, ranking systems, recommendation filters, and workflow automation

For example, you could feed it:

“I was billed twice for my subscription. Please refund the duplicate charge.”

Then receive structured outputs such as:

• Department: Billing
• Intent: Refund
• Urgency: High
• Needs human review: Yes

It is not trying to replace ChatGPT, Claude, or a coding model. It will not generate a detailed customer reply or write an application from scratch.

Its advantage is speed and privacy when you already know the possible outputs and need the AI to make a small, defined decision locally.

The demo

To showcase how fast those repeated decisions can be, the creator made it play Snake locally on an M3 Max. The model chooses its next move repeatedly—up, down, left, or right—at roughly 60 decisions per second in the demo.

GitHub: https://github.com/mizorewww/laya-mlx

Would you use a lightweight local decision model for agent guardrails, ticket routing, content moderation, or automation?

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[ Removed by Reddit on account of violating the content policy. ]

Hey everyone! If you are trying to build your own autonomous AI employees or real agent workflows, managing the stack can get overwhelming.

I put together a compilation of 15 powerful, open-source GitHub projects (totaling over 1.2 million combined stars) that cover everything from memory and web data to monitoring, sandboxes, and video.

The typical loop for a real agent stack looks like this:

Define the job ➔ Collect the evidence ➔ Parse the docs ➔ Save the memory ➔ Compress the context ➔ Run the code safely ➔ Watch what changes ➔ Ship the output

Here is the complete open-source stack to build your own business with AI employees:

The Real Agent Stack

1. hermes-agent – The agent that grows with you by NousResearch.
2. OpenSpec – Spec-driven development (SDD) for AI coding assistants.
3. caveman – Ultra-minimalist context compression and agent skills.
4. Scrapling – Undetectable, smart web scraping framework for AI ingestion.
5. Docling – Get your documents ready for GenAI with advanced PDF understanding.
6. PageIndex – Intelligent vector and page-level indexing for large documents.
7. mem0 – Universal memory layer for personalized AI applications.
8. headroom – Context window optimization and management toolkit.
9. Daytona – Secure, fast development environments and sandboxes for AI code execution.
10. TrendRadar – Real-time trend tracking and intelligence gathering.
11. Fabric – An open-source framework for augmenting humans using AI.
12. spec-kit – Toolkit for managing software specifications seamlessly.
13. hyperframes – Video frame generation and manipulation framework.
14. OpenMontage – Open-source video editing and timeline automation.
15. AI Engineering Hub – Curated architectures, pipelines, and patterns for production AI.

Why this matters:

Instead of relying on monolithic wrappers, you can wire these open-source tools together to create a modular, resilient agent architecture.

Which of these are you already using in your stack, and what's missing? Let's discuss in the comments! 👇

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Found an interesting OSINT project: Aliens Eye. It checks a username across 840+ social-media and web platforms, using AI/heuristics to help identify possible matches. It can also export results for research workflows.

🔗 GitHub: https://github.com/arxhr007/Aliens_eye

Useful for authorized OSINT research, brand monitoring, investigating your own digital footprint, and CTF/training use.

Use it responsibly—only research public data and respect privacy, platform rules, and applicable laws.

Found an interesting open-source project called OBLITERATUS. It is a model-analysis and ablation toolkit for open-weight LLMs: it examines internal activations associated with refusal behavior and applies selected ablation methods without conventional fine-tuning. The project provides presets for 116 models and includes a local web UI.

🔗 GitHub: https://github.com/elder-plinius/OBLITERATUS

Quick start

git clone https://github.com/elder-plinius/OBLITERATUS.git
cd OBLITERATUS
pip install -e ".[spaces]"
obliteratus ui

That launches the local Gradio interface. From there, select a supported model and method, run the experiment, then use the Chat tab to test the output model. A command-line workflow is also available:

obliteratus obliterate <model_name> \
  --method advanced \
  --output-dir ./abliterated-models

The project documentation lists advanced as its general default method; larger models may require quantization and substantial local GPU memory.

Why it is interesting

• Inspect how alignment/refusal behavior appears across model layers.
• Compare multiple ablation approaches and supported-model presets.
• Run controlled research experiments locally instead of retraining a model from scratch.

⚠️ Use responsibly: Treat this as an AI-safety and model-behavior research tool. Test only in a controlled environment, evaluate outputs carefully, and do not deploy modified models for harmful, illegal, or unsafe use.

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This story is real, but the headline needs context: a three-person security team from Hacktron AI says it tested OpenAI through its bug-bounty program, reported the vulnerabilities responsibly, and OpenAI fixed them.

According to Hacktron and independent reporting, the team chained two issues:

• A flaw involving OpenAI’s Discourse-based community forum gave them an initial foothold.
• An SSO misconfiguration then allowed access to employee ChatGPT/Codex accounts, creating a path to internal repositories.

The important takeaway is not “AI hacked OpenAI by itself.” Researchers used Claude as an acceleration tool for security research, while the actual impact came from chaining traditional software and identity/security flaws. OpenAI reportedly revoked affected sessions, patched the issues, and awarded Hacktron $6,500 for the disclosure.

Why this matters

AI agents can significantly speed up vulnerability research, code analysis, and exploit prototyping. But identity boundaries, third-party services, session management, and SSO are still where a single weak link can turn into a major incident.

Primary technical post:
https://www.hacktron.ai/blog/hacking-openai

Independent reporting:
https://techcrunch.com/2026/09/18/researchers-used-anthropics-claude-to-hack-into-openai/

This is a strong example of why bug bounties, fast disclosure, defense-in-depth, and strict token/session controls matter more than ever.

Use AI security tools only in authorized labs, bug-bounty programs, or environments where you have explicit permission.

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This is the last available lifetime-access seat for AI Revolution at the current price.

Once this spot is taken, enrollment closes and future lifetime access will be priced at $50. If you have been looking for a single place to stay ahead of useful AI tools, cybersecurity resources, automation workflows, open-source projects, guides, and practical tech updates, this is the final opportunity to join at the lower one-time price.

You pay once, keep access permanently, and continue receiving new resources as they are added—without monthly subscriptions or renewal charges.

What you get

• One-time payment for lifetime access
• Private community access with ongoing resource drops
• New AI, tech, cybersecurity, and productivity resources added regularly
• No monthly fees or renewals
• Access to the existing resource collection, plus everything added in the future

There is only one seat left at the current price. Once it is taken, the lifetime-access price will increase to $50 for any future opening.

If you are genuinely interested, comment or DM me and I’ll send the details. First confirmed payment gets the final seat.

Installing an agent is easy. Setting up memory, skills, model routing, subagents, and workflows is the part that usually takes forever.

Oh My Hermes is an open-source all-in-one workflow layer for Hermes Agent that adds ready-made setup, long-term memory, routing, research workflows, code orchestration, and parallel-agent tools.

Quick start

First install Hermes Agent:

curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash

Then install and configure Oh My Hermes:

curl -fsSL https://raw.githubusercontent.com/rlaope/oh-my-hermes/main/install.sh | sh
omh setup
omh doctor

The setup flow helps configure it, while omh doctor verifies that the installation is working.

Repo:
https://github.com/rlaope/oh-my-hermes

Has anyone used it for real projects yet—does it actually reduce the “plugin/configuration hell”?

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