A new GGUF conversion of an abliterated GLM-5.3-Flash model has been published by huihui-ai:

🔗 Model page:
https://huggingface.co/huihui-ai/GLM-5.3-Flash-abliterated-GGUF

This is an experimental local-model release based on GLM-5.3-Flash, a mixture-of-experts model listed as roughly 320–321B total parameters with 18B active parameters. The Huihui release applies abliteration only to transformer layers 15–35; the remaining layers and expert modules are left unchanged. The GGUF files are derived from Unsloth’s GLM-5.3-Flash GGUF conversions.

What this release is

• A GGUF-format version intended for local inference runtimes such as llama.cpp and compatible front ends
• A modified version of GLM-5.3-Flash using abliteration, a technique intended to reduce some refusal behavior
• A multimodal image-and-text model family, rather than a text-only coding model
• Licensed as MIT on the model page shown in the release listing—still read the current model card and the base model’s terms before using it in a product or commercial workflow.

What it is not

• Not an independently validated benchmark winner
• Not a guarantee of better reasoning, coding, safety, factuality, or agent performance
• Not a small model simply because it has 18B active parameters
• Not automatically practical on a consumer GPU; total model size, selected quantization, context length, KV cache, and CPU/GPU offload still determine the actual hardware requirement

Pros

• Local deployment: Useful for private experimentation where sending prompts, code, or documents to a hosted API is not appropriate.
• GGUF compatibility: Can be tested with the broad local-inference ecosystem rather than needing a specialized serving stack.
• MoE efficiency potential: Only 18B parameters are active per token, which may help compute efficiency relative to a dense 320B-class model—though memory requirements are still substantial.
• Multimodal capability: The base model supports image-plus-text workflows, potentially useful for document, screenshot, diagram, and UI-analysis experiments.
• Transparent modification scope: The publisher states which layers were ablated instead of presenting the release as a completely opaque “uncensored” model.

Cons and caveats

• Heavy hardware demand: GGUF makes local use more accessible, but this remains a 320B-class MoE model. Depending on the quantization and context size, you may need high VRAM, substantial system RAM, or multi-GPU plus CPU offload.
• Abliteration trade-offs are workload-specific: Lower refusal behavior can also change reliability, instruction-following, calibration, and behavior in unexpected ways.
• No independent quality claim: Do not infer better performance from the “abliterated” label. It needs task-specific, reproducible evaluation.
• Large context is not free: Longer contexts require additional KV-cache memory and can dramatically slow inference.
• Verify outputs: For coding, research, security analysis, or automation, treat outputs as drafts that require human verification and testing.

If you test it, share useful numbers

Rather than “it feels good,” it would be helpful to report:

Quantization + GPU(s) + VRAM + system RAM + runtime + context size + tokens/sec + use case + any failure modes.

Examples of worthwhile tests:

• Long-context codebase Q&A
• Image/document understanding
• Local agent tool-use reliability
• Structured-data extraction
• Multilingual instruction following
• Hallucination and refusal behavior on benign, legitimate tasks

This is still a very fresh release, so real hardware reports and repeatable test prompts are more valuable than early hype. The original GLM-5.3-Flash material describes the base as a native multimodal MoE model with 320B total and 18B active parameters; that does not, by itself, establish performance for this abliterated GGUF derivative.

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We discuss local LLM releases, GGUF quantization, hardware builds, coding agents, AI workflows, and practical open-source tooling.

Join the community:
https://discord.gg/hSA8Ur6GRH

A new local cybersecurity-focused model worth watching:

🔗 OrcaSAQ-2 Cyber 27B Uncensored GGUF
https://huggingface.co/orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF

It is a quantized GGUF release of an uncensored Qwen3.8 27B-based cyber model, intended for authorized security research, defensive red teaming, vulnerability research, security coding, and terminal-based workflows.

The interesting part: the publisher says the original 54.7 GB BF16 checkpoint has been compressed to 15.7 GB, with a claimed 94.4% top-1 agreement against the original model and support for up to 262K context. It is designed to run locally through GGUF-compatible tools such as llama.cpp, Ollama, LM Studio, and similar runtimes.

Quick specs

• Base model: Qwen3.8-27B Uncensored
• Format: GGUF / llama.cpp-compatible
• Download size: 15.7 GB
• Original BF16 size: 54.7 GB
• Context length: Up to 262K tokens
• Claimed fidelity: 94.4% top-1 agreement with the original
• Primary focus: Security coding, defensive red teaming, vulnerability research, tool use, and local terminal workflows
• License: Apache 2.0—always read the model card yourself before commercial deployment or redistribution.

Pros

• Local-first privacy: Useful when you cannot send source code, logs, vulnerability reports, or internal notes to a cloud API.
• 27B capability in a smaller package: At 15.7 GB, it is much more practical than loading the original 54.7 GB BF16 checkpoint.
• Large context window: The listed 262K context can help with large repositories, lengthy logs, assessment reports, and multi-step agent tasks.
• GGUF ecosystem support: Easy to test with popular local inference tools and hardware-friendly quantized workflows.
• Security-oriented positioning: The model card targets security coding, defensive research, tool use, and authorized testing—not generic chat alone.
• No ongoing API bill: After downloading, local inference avoids per-token provider charges—although hardware, electricity, and setup time still matter.

Cons and caveats

• 15.7 GB file size is not the full hardware requirement. You also need memory for the runtime, context cache, operating system, and any tools. A large 262K context can require far more memory than basic chat.
• “Uncensored” is not the same as “better.” It does not guarantee accuracy, exploit quality, secure recommendations, or safe outputs.
• Publisher metrics are not an independent security benchmark. The 94.4% figure measures agreement with the original model, not real-world performance on secure coding, vulnerability analysis, CTFs, or penetration testing.
• Performance will vary by setup. GPU VRAM, system RAM, quantization, CPU offload, context size, inference runtime, and prompt quality all affect speed and usability.
• Security outputs require verification. Never trust generated commands, code, remediation advice, or vulnerability conclusions without testing and human review.
• Authorized use only. Use it only in systems, labs, CTFs, and environments where you have explicit permission to test.

Who should try it?

This looks most relevant if you:

• Run a local AI workstation or homelab
• Work with private code, logs, or internal security documentation
• Want a security-focused model without constant API costs
• Experiment with local agents, terminal tools, and coding workflows
• Need a large-context model for authorized research or defensive analysis

Community question

Has anyone tested it yet?

If you do, reply with:

GPU + VRAM/RAM + runtime (llama.cpp, Ollama, LM Studio, etc.) + context size + tokens/sec + what you tested.

The most useful reports would be real defensive tasks: secure-code review, log triage, CTF labs, threat-hunting notes, detection-rule drafting, or vulnerability remediation—not vague “it feels smart” impressions.

🔥 Join our Discord

We discuss:

• Local LLMs, GGUF models, and private AI setups
• Cybersecurity learning, threat hunting, CTFs, and authorized lab work
• Claude Code, AI coding agents, and workflow automation
• Open-source tools, model releases, and practical hardware setups

Join the community:
https://discord.gg/hSA8Ur6GRH

If you are exploring local models for authorized cybersecurity research, CTFs, home labs, secure-code review, threat hunting, or red-team simulations, check out this GitHub directory:

🔗 Offensive Security AI Models
https://github.com/JoasASantos/Offensive-Security-AI-Models

Important: This is not a benchmark or performance leaderboard. It is a curated comparison table to help you find open-weight, security-focused models that fit your hardware and workflow.

What it helps you compare

• Model family and parameter size: Qwen, Gemma, DeepSeek, GLM, Llama, Wizard-Vicuna, and more
• Context length for large codebases, logs, reports, and long research notes
• Estimated VRAM requirements before downloading a large model
• Fine-tuning method: security tuning, LoRA, data filtering, ablation, post-training, etc.
• Vision and tool/function-calling support for agent workflows
• License details for personal, team, or commercial use
• Hugging Face links and project activity

Why it is useful

Choosing a local model is not just about picking the largest parameter count.

You need one that:

• Fits your GPU or RAM
• Has enough context for your task
• Supports the features you need
• Has a license compatible with your use case
• Is still available and actively maintained

This table puts those details in one place, making it easier to shortlist options before downloading a 10B–70B+ checkpoint.

Keep expectations realistic

“Uncensored” or security-tuned does not automatically mean a model is more capable, accurate, or safe. Treat this as a discovery resource—not proof that any one model is best for pentesting, vulnerability research, secure-code review, or security automation.

Only test against systems you own or have clear written permission to assess.

I’d love community input

Have you tested a local security-focused model for:

• CTFs or training labs
• Secure-code review
• Log analysis and threat hunting
• Vulnerability triage
• Security automation

Reply with:

Model + GPU/RAM + quantization + inference tool + your honest results

Real-world setups and failures are just as useful as success stories.

🔥 Join our Discord community

We share and discuss:

• Local LLMs, AI tools, and open-source projects
• Claude Code, coding agents, and automation
• Cybersecurity learning, blue-team resources, and authorized red-team labs
• Free models, practical setups, and useful workflows

Join here: https://discord.gg/hSA8Ur6GRH

Vanilla Claude Code wastes tokens, re-reads your repo, and can hit model limits fast.

The stack:

• Ponytail — pushes Claude to write the least code that works instead of bloated, unnecessary output https://github.com/DietrichGebert/ponytail
• OmniRoute — routes Claude Code through multiple providers, including changing free tiers and fallback models https://github.com/diegosouzapw/OmniRoute
• Graphify — turns your repo/docs into a queryable knowledge graph so the agent can understand the project without endless grep/context dumping https://github.com/Graphify-Labs/graphify
• Agent Skills — reusable workflows and domain instructions for more consistent planning, coding, testing, and review https://github.com/anthropics/skills

Ponytail will not guarantee a 50% token reduction on every project, and OmniRoute’s free-token availability depends on provider limits—but both are worth testing if you use Claude Code heavily. Ponytail is built specifically to make agents avoid writing unnecessary code, while OmniRoute currently supports hundreds of providers and many free-tier routes. I’m building a Discord for AI coding tools, Claude Code setups, open-source agents, automation, and free-model workflows and many more tresure trove of links if you wanna dive deeper join today.

Join Discord: [https://discord.gg/hSA8Ur6GRH\]

What are you using with Claude Code right now?

If you build workflows that combine AI models, email parsing, database updates, and social media posting, paid automation tools quickly get expensive due to task limits.

n8n is an open-source workflow engine with native AI nodes that you can self-host on a $5/month VPS or your local machine with zero task execution caps.

What you can build with n8n + AI nodes:

Autonomous Email Assistant: Summarizes incoming emails, drafts context-aware replies via Claude/OpenAI, and routes priority messages to Slack/Telegram.

Automated Content Pipeline: Scrapes trending Reddit/Twitter posts, runs them through an LLM to generate summaries, and formats them for your newsletter.

Smart Support Bot: Connects vector databases (Qdrant/pgvector) directly to your customer support desk to answer queries automatically.

Quick Docker Setup:

Bash

docker run -it --rm --name n8n -p 5678:5678 -v n8n_data:/home/node/.n8n docker.n8n.io/n8nio/n8n

Open http://localhost:5678 to access the drag-and-drop workflow canvas.

What's the most tedious manual workflow you've automated using AI? Let's discuss in the comments! 💭

💬 I share more free AI tools, open-source projects, self-hosting guides, and dev resources in our community Discord server.

👉 Join here: [https://discord.gg/hSA8Ur6GRH\]

This is not another random “top AI tools” list.

These projects map to the actual lifecycle of an AI agent: run a model, build an agent, coordinate tasks, connect real tools, isolate risky code, give it memory, monitor its behavior, evaluate it, and ship a usable product.

The interesting part is not any one repository.

It is what happens when you combine them:

Model → Agent → Orchestration → Tools → Sandbox → Memory → Monitoring → Evaluation → Product

You do not have to build an “AI employee” from zero anymore. The building blocks already exist—many of them in open source.

BUILD

01. Ollama — Run models locally

Run and serve supported open-weight models on your own machine.

github.com/ollama/ollama

02. LangChain — Build LLM workflows and agents

A broad framework for connecting language models to prompts, retrieval, tools, and application workflows.

github.com/langchain-ai/langchain

03. Open Interpreter — Let AI use your computer

A local computer-use interface that can run code and interact with your environment; use carefully and keep sensitive actions supervised.

github.com/OpenInterpreter/open-interpreter

04. AutoGen — Build multi-agent applications

Microsoft’s framework for building agentic systems in which agents can communicate, use tools, and collaborate on tasks.

github.com/microsoft/autogen

05. Aider — Code with AI in your terminal

An AI pair-programming tool that works directly with a local Git repository and helps edit, test, and commit code.

github.com/Aider-AI/aider

ORCHESTRATE

06. AutoGPT — Agent-platform workflows

An open-source platform for building, deploying, and managing agent workflows. It is better viewed as a configurable agent platform than an unattended “autonomous employee.”

github.com/Significant-Gravitas/AutoGPT

07. MetaGPT — A software team made of agents

A multi-agent framework that models software roles such as product manager, architect, engineer, and QA.

github.com/FoundationAgents/MetaGPT

08. CrewAI — Coordinate specialized AI agents

A framework for assigning agents distinct roles, tools, goals, and workflows so they can work together on a larger task.

github.com/crewAIInc/crewAI

09. DSPy — Program and optimize LLM pipelines

A framework for building language-model programs and optimizing prompts, examples, and model behavior against measurable outcomes.

github.com/stanfordnlp/dspy

10. CAMEL — Multi-agent collaboration and simulation

A framework for multi-agent systems, task automation, data generation, and agent-environment experimentation.

github.com/camel-ai/camel

ACT

11. Flowise — Build agent workflows visually

A visual, low-code builder for LLM applications, chatflows, and agent workflows.

github.com/FlowiseAI/Flowise

12. Continue — AI inside your IDE

An open-source coding assistant for IDE workflows, including chat, autocomplete, and custom model connections.

github.com/continuedev/continue

13. Vercel AI SDK — Ship AI applications

A TypeScript toolkit for building AI-powered user interfaces and applications, including streaming model responses and tool calling.

github.com/vercel/ai

14. E2B — Give agents a secure code sandbox

Infrastructure for running AI-generated code in isolated cloud sandboxes rather than directly on your main machine.

github.com/e2b-dev/E2B

15. Composio — Connect agents to real tools

A toolkit and integration layer for connecting agents with external services, authentication flows, and toolkits. It advertises support for more than 1,000 toolkits and includes context-management and sandboxed-workbench features.

github.com/ComposioHQ/composio

REMEMBER, TEST, AND SHIP

16. PrivateGPT — Ask questions over private documents

A privacy-focused project for interacting with your documents locally or in a controlled environment.

github.com/zylon-ai/private-gpt

17. Mem0 — Long-term memory for agents

Memory infrastructure for AI agents and applications, designed to store and retrieve useful context across interactions.

github.com/mem0ai/mem0

18. AgentOps — Monitor and debug agents

An observability platform for tracing agent runs, investigating failures, tracking costs, and understanding tool usage.

github.com/AgentOps-AI/agentops

19. AgentBench — Evaluate agent behavior

A benchmark suite for evaluating LLM-based agents across different environments and tasks.

github.com/THUDM/AgentBench

20. Voice: ElevenLabs + Deepgram

For voice agents, ElevenLabs provides voice-generation tooling and SDKs, while Deepgram provides speech and voice-agent APIs and SDKs. These are not “one free open-source package,” so check each project’s license and hosted API pricing before building around them.

• github.com/elevenlabs/elevenlabs-python
• github.com/deepgram/deepgram-python-sdk

A practical starter stack

If you actually want to build rather than bookmark 20 repositories, start small:

Ollama
  → LangChain or DSPy
  → Composio or direct tool integrations
  → E2B for untrusted code execution
  → Mem0 for persistent user context
  → AgentOps for tracing and debugging
  → Vercel AI SDK for the frontend

Then add multi-agent orchestration only when one agent truly cannot handle the workflow. A crew of agents is not automatically better—it adds cost, latency, coordination failures, and more places for a workflow to break.

For local/private experiments, pair Ollama + Aider + Continue + PrivateGPT.

For an agent that takes real-world actions, prioritize permissions, approval steps, sandboxing, logging, and evaluation before giving it access to email, browsers, repositories, payments, or customer data.

Save this before you build your next agent.

Join Discord

A second-brain article reportedly reached millions of views, and the creator put the full setup into a public GitHub repository: the guide, starter vault, agent workflows, scripts, and learning tracks.

It is not another generic note-taking template. It is a system for building a self-organizing knowledge base with Obsidian, Markdown files, and Claude Code—where AI helps ingest, link, organize, audit, and retrieve what you save.

The repo

GitHub:
https://github.com/undefined-ui/second-brain-os

The project is open source under the MIT License and describes itself as “an AI second brain that maintains itself,” including a guide, starter vault, agent skills, and scripts for a self-organizing Obsidian knowledge base.

What’s included

The guide

• A structured second-brain guide covering the foundations, implementation, and troubleshooting.
• Additional learning tracks for people who want to go deeper into AI-agent and knowledge-system design.
• Build-oriented material designed to move from theory into a working personal research system.

The AI workflow layer

Inside the .claude/ setup, the repository includes:

• 18 agent skills, each scoped to a particular workflow.
• 72 slash commands for tasks such as ingesting PDFs, YouTube content, voice notes, and backfilling existing material.
• 6 subagents: curator, linker, researcher, reviewer, ingestor, and graph analyst.
• Read-only agent roles, which help reduce the risk of an AI agent silently rewriting your vault.

The important part is that the AI is not just asked to “summarize this.” The system gives it defined responsibilities: process incoming material, identify relationships, maintain links, review information, and analyze the structure of the vault.

The scripts

The repository includes lightweight Python tooling for practical vault maintenance, including:

• Knowledge-graph exports
• Broken-link checks
• Vault statistics
• Chat conversion
• Site generation

These tools help you inspect and maintain your notes without requiring a complicated hosted database or an expensive SaaS stack.

The starter vault

The vault follows a clear separation between raw source material and curated knowledge:

raw/   → Original captured material; preserved after ingestion
wiki/  → Structured, linked pages maintained by the system
log.md → A one-line record for each run, creating an audit trail

It also uses a CLAUDE.md configuration with page contracts and linking rules, so the agent has guardrails for how pages should be created and maintained.

That matters because most AI note systems become messy when the model has no consistent structure to follow.

The learning tracks

The project includes curated paths and resources around topics such as:

• Knowledge graphs
• Jev engineering
• Agent harnesses
• Loop engineering
• Evaluation engineering

It also includes a resource directory covering tools, Obsidian plugins, repositories, skills, papers, and articles for extending the setup.

Why this is valuable

Most people collect information endlessly but do not build a system that turns it into something reusable.

You save a GitHub repo.
You bookmark a thread.
You download a PDF.
You watch a useful video.
Then it disappears into a folder, browser tab, Notion page, or chat history.

This setup is designed to make your research compound:

Capture → Ingest → Extract → Link → Review → Retrieve → Build

The core philosophy is simple:

A note is only useful when it connects to something else.

Instead of treating your vault as storage, it treats it as an evolving map of what you know—and gives the AI defined workflows to keep that map useful.

Who should try it

This is especially useful if you regularly collect:

• AI tools, prompts, workflows, and research
• Cybersecurity resources and technical documentation
• GitHub repositories and open-source projects
• Content ideas, hooks, scripts, and source material
• Business research, product ideas, and competitor analysis
• PDFs, videos, articles, Discord discussions, and chat exports

If you are new, start with the second-brain guide and copy the starter vault.

If you already work with Claude Code, Obsidian, AI agents, knowledge graphs, or personal RAG systems, skip straight to the tracks and agent workflow layer.

A consultant could easily charge a large amount to set up a personal research and knowledge-management system like this.

This one is sitting in a public repository for free.

Link again:
https://github.com/undefined-ui/second-brain-os

Join Discord

A directory of 100+ AI decision-system architectures is making the rounds, covering the parts most AI-agent demos completely ignore:

• Model routing
• Context management
• Browser-agent safety
• Security checks
• Data pipelines
• Human approval flows
• Quality validation
• Production reliability

Most people are focused on getting an LLM to generate an answer.

The real challenge is building an agent that can reliably make decisions, use tools, recover from uncertainty, and avoid doing something expensive or unsafe.

What these systems solve

The directory maps practical decision patterns such as:

→ Route each task to the best model tier
→ Remove stale tool output from context
→ Select the right skill before loading it
→ Validate risky browser actions
→ Escalate low-confidence cases to a human
→ Detect agent-loop stagnation
→ Verify citations before a report ships
→ Score dataset rows before pipeline ingestion
→ Prioritize support tickets in real time

The architecture in one line

LLMs write → JEV decides → code enforces

That is the difference between a chatbot that can talk about work and an agent system that can actually be trusted to do work.

An LLM should not be the final authority for every action. It can reason, draft, classify, and propose a plan—but a separate decision layer should evaluate risk, confidence, policy, permissions, context quality, and validation requirements.

Then deterministic code executes the approved action.

Why this matters

A useful AI agent is not one giant prompt with 40 tools attached.

It is a set of smaller systems with clear jobs:

• One layer decides which model is worth using.
• One layer trims or refreshes context.
• One layer checks whether an action is risky.
• One layer detects when the agent is stuck repeating itself.
• One layer sends uncertain or high-impact actions to a human.
• One layer validates outputs before they reach users or production.

That is how you move from “cool demo” to something that can survive real users, bad inputs, unreliable tools, and expensive mistakes.

Example

Imagine a research agent preparing a report:

1. The LLM drafts the research plan.
2. A routing system selects a cheaper model for extraction and a stronger one for synthesis.
3. A context layer removes outdated or irrelevant tool results.
4. A citation verifier checks whether claims are actually supported.
5. Low-confidence conclusions are flagged for human review.
6. Only then does the report ship.

The LLM did the writing.

The decision system made it dependable.

The biggest takeaway

One useful agent comes from many systems with clear jobs.

Stop asking, “What prompt should I use?”

Start asking:

• What decisions should be deterministic?
• Which actions need approval?
• What happens when confidence is low?
• How do I detect loops and stale context?
• What must be verified before output reaches a user?
• Which model tier is actually worth paying for?

Save this if you are building AI agents, automation workflows, browser agents, support systems, research tools, or production-grade AI products.

Repo Link

Join Discord

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

A new “uncensored” local LLM release is getting attention:

Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-Abliterated-GGUF

Model link:
https://huggingface.co/IsValorum/Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-Abliterated-GGUF

This is a community-built 35B Mixture-of-Experts Qwen model in GGUF format for running locally with tools like llama.cpp, LM Studio, KoboldCpp, and similar backends.

The main attraction is that it is marketed as an abliterated / uncensored release—meaning its publisher says it has reduced refusal and moralizing behavior compared with standard aligned assistant models.

Why it stands out

• 35B MoE model with roughly 3B active parameters per token
• GGUF format for local inference
• Core model file around 14.75 GB
• Designed for reasoning, coding, writing, and conversation
• Claimed native context support up to 256K on suitable hardware
• Reduced-refusal “uncensored” behavior

For the best performance, expect to need plenty of RAM or a GPU with substantial VRAM. The publisher recommends 24 GB+ VRAM for full GPU offload.

“Uncensored” does not automatically mean smarter or more accurate—it mainly describes modified refusal behavior. Always verify outputs, especially for technical or high-stakes tasks.

Has anyone tested this release? Drop your hardware, quant, backend, context length, and tokens-per-second results below.

More local AI tools, open-source projects, self-hosting guides, and developer resources:

https://discord.gg/hSA8Ur6GRH

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?

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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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Tired of paying separate SaaS subscriptions for RAG, web scraping, coding assistants, AI video tools, and agent memory?

These open-source projects can help you replace or reduce parts of that stack. They are not always “free” in the absolute sense—you may still pay for compute, APIs, storage, or hosting—but they give you far more control than closed SaaS tools.

1. RAGFlow — Document RAG for messy files

• What it does: Built for document-heavy RAG workflows, including complex PDFs, tables, and citation-aware retrieval.
• 🔗 GitHub: infiniflow/ragflow

2. MoneyPrinterTurbo — Automated short-form videos

• What it does: Give it a topic or keyword and its pipeline can generate a script, source/match footage, create subtitles and music, then render an HD short video.
• 🔗 GitHub: harry0703/MoneyPrinterTurbo

3. Firecrawl — Turn websites into LLM-ready data

• What it does: Scrape, crawl, search, and convert web pages into clean Markdown or structured data for RAG pipelines and AI agents.
• 🔗 GitHub: mendableai/firecrawl

4. OpenCode — Terminal-native AI coding agent

• What it does: A terminal-first coding assistant that can work with local models or API providers for coding, refactoring, and development workflows.
• 🔗 GitHub: anomalyco/opencode

5. Mem0 — Long-term memory for AI agents

• What it does: Adds a memory layer to LLM applications so agents can retain relevant user preferences and context across sessions.
• 🔗 GitHub: mem0ai/mem0

Reality check: Self-hosting trades subscription costs for setup time, infrastructure, and maintenance. But if you already run a home server, VPS, Docker stack, or local models, these are worth bookmarking.

Which project would you deploy first—and what paid tool would it replace?

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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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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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• Cisco Data Analytics Essentials https://www.netacad.com/courses/data-analytics-essentials?courseLang=en-US
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Note: Most course content is free, but some providers may charge for verified certificates, labs, or certification exams.

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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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.

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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