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T0nd3: Revolutionizing AI Tools on GitHub
T0nd3: Revolutionizing AI Tools on GitHub The landscape of artificial intelligence (AI) tools is evolving rapidly, and platforms like GitHub are at the forefron…
AI Tool: Uvic-Aurora's GitHub Repo Highlighted on Hacker News
Exploring Uvic Aurora: A Standout AI Tool Highlighted on Hacker News Uvic Aurora, an innovative AI tool, has caught the attention of the tech community after be…
AI Tools: Simon Willison's Latest Innovations on Hacker News
AI Tools: Simon Willison's Latest Innovations on Hacker News Simon Willison, a prominent figure in the tech community, has recently unveiled a suite of AI tools…
AI Tool: PythonGiant's GitHub Repository Highlighted on Hacker News
Exploring PythonGiant's GitHub Repository: A Spotlight on Innovation PythonGiant, a notable repository on GitHub, has garnered significant attention and praise …
NuExtract3: Advanced AI Tool for Data Extraction
NuExtract3: Revolutionize Data Extraction with Advanced AI In the rapidly evolving landscape of data management, NuExtract3 emerges as a cutting edge tool desig…
AI Tool: Uberdosis on GitHub - Hacker News
Discover Uberdosis: The Powerful AI Tool Hosted on GitHub Uberdosis, an innovative AI tool available on GitHub, is making waves in the tech community. This vers…
Mupt AI: Revolutionizing GitHub with Advanced AI Tools
Mupt AI: Revolutionizing GitHub with Advanced AI Tools Mupt AI is at the forefront of integrating advanced artificial intelligence into GitHub, offering an arra…
Struct AI: Revolutionizing Data Structures with GitHub
StructAI: Transforming Data Structures with GitHub StructAI is a cutting edge data structure library designed to simplify and enhance data management tasks. Lev…
AI Tool: GitHub's Rajatarya for Enhanced AI Development
AI Tool: GitHub's Rajatarya for Enhanced AI Development In the rapidly evolving landscape of artificial intelligence (AI), developers continually seek robust to…
GPUBook.io: Revolutionizing AI Tools with GPU Power
GPUBook.io: Transforming AI Tools with GPU Technology In the rapidly evolving world of artificial intelligence (AI), computational power is paramount. GPUBook.i…
AI-Powered Pose Estimation with SetPose.com
AI Powered Pose Estimation with SetPose.com SetPose.com offers cutting edge AI powered pose estimation solutions designed for users across diverse fields. This …
Lakonik/AsymFLUX.2-klein-9B: Revolutionizing AI on Hugging Face
Lakonik/AsymFLUX.2 klein 9B: Transforming AI on Hugging Face The AI community is buzzing with the introduction of Lakonik/AsymFLUX.2 klein 9B, a groundbreaking …
Lance: ByteDance's New AI Tool on Hugging Face
Lance: ByteDance's New AI Tool on Hugging Face In the ever evolving landscape of artificial intelligence (AI), ByteDance, the parent company of TickTock and Tik…
AI Tool for Code Generation: GitHub's Kouhxp
AI Tool for Code Generation: Harnessing GitHub's Kouhxp GitHub's Kouhxp stands out as a revolutionary AI tool, empowering developers to generate code efficientl…
Thinking Machines' Cheap-IM: AI Demo on a CPU Laptop
Exploring Thinking Machines' Cheap IM: AI on a CPU Laptop Thinking Machines has introduced Cheap IM, an innovative AI demo that showcases the impressive capabil…
Discover xalgorithm.xyz: Innovative AI Tool on Hacker News
Unveiling xalgorithm.xyz: The Cutting Edge AI Tool Taking Hacker News by Storm In the dynamic realm of Hacker News, xalgorithm.xyz has gained significant tracti…
Browser-Based AI Synthesizer, Drum Machine, and Sequencer
Browser Based AI Synthesizer, Drum Machine, and Sequencer Browser based AI tools for music production are revolutionizing the way artists create and enhance the…
Neural Net Learns to Play Snake: Watch the AI Progress
Neural Net Learns to Play Snake: Witness the AI Progression The world of artificial intelligence (AI) is continually expanding, with new applications and advanc…
FrontiersMind Nandi-Mini-600M Early Checkpoint: AI Tool on Hugging Fac
Exploring FrontiersMind Nandi Mini 600M Early Checkpoint on Hugging Face FrontiersMind Nandi Mini 600M Early Checkpoint, available on Hugging Face, marks a sign…
Dagraph.com: Revolutionizing AI Tools on Hacker News
Dagraph.com: Transforming AI Tools on Hacker News In the rapidly evolving realm of AI, Dagraph.com stands out as a pioneering platform, garnering significant at…
AI Tool: GitHub's New AI-Powered Code Assistant
Harnessing Innovation: Exploring GitHub's Latest AI Powered Code Assistant GitHub has launched a groundbreaking new tool in the realm of code assistance, design…
Drew Baglino Launches Heat Pump Startup Sadi Thermal Machines
Sadi Thermal Machines is Drew Baglino's second startup since leaving Tesla in 2024.
Adaption's AutoScientist: AI Tool for Rapid Model Training
Adaption's new AutoScientist tool is designed to let models adapt to specific capabilities quickly through an automated approach to conventional fine-tuning.
AI Tank Training: $100 in Claude Tokens, 1k Battles
AI Tank Training: $100 in Claude Tokens, 1k Battles In the realm of AI training, simulated environments offer a cost effective and flexible approach to enhance …
3D Reconstruction AI Tool: ArthurBrussee/brush on GitHub
3D Reconstruction for all
Profine: Optimize PyTorch Training Loops on Real GPUs
Profine: Efficient PyTorch Training Loops on Real GPUs In the fast evolving landscape of machine learning, optimizing training loops in PyTorch is crucial for e…
Statewright: Visual State Machines for Reliable AI Agents
Visual State Machines for Reliable AI Agents: A Statewright Review Introduction to Statewright Statewright is a revolutionary tool that enables the creation of …
AI Tool Tack.pics Simplifies Image Management with AI
AI Tool Tack.pics: Revolutionizing Image Management with Advanced Technologies Introduction In the modern landscape, managing images efficiently is crucial for …
Anima AI Tool: Revolutionizing Text Generation on Hugging Face
Anima AI Tool: Transforming Text Generation on Hugging Face The landscape of text generation is rapidly evolving, and one of the cutting edge tools leading this…
Gemma-4-31B: Hugging Face's New AI Tool with DFlash Integration
Discovering Hugging Face's Latest Innovation: Gemma 4 31B with DFlash Integration Hugging Face has unveiled a ground breaking tool in the realm of artificial in…
SulphurAI/Sulphur-2-Base: New AI Tool on Hugging Face
Discover SulphurAI's Sulphur 2 Base: A New AI Tool on Hugging Face Introduction SulphurAI has introduced Sulphur 2 Base, a novel AI tool available on Hugging Fa…
Pranjolm AI Tool: New Innovations on GitHub
Pranjolm AI Tool: Groundbreaking Innovations on GitHub Pranjolm, an open source AI tool recently launched on GitHub, introduces revolutionary features. Designed…
MLJAR Superwise: AI Tool for Data Labeling and Annotation
MLJAR Superwise: Revolutionizing Data Labeling and Annotation MLJAR Superwise is a cutting edge AI tool designed to streamline the processes of data labeling an…
Mljar Studio: Local AI Data Analyst Saving Notebooks
Mljar Studio: Empowering Local AI Data Analysis Mljar Studio is a cutting edge, open source tool tailored for local AI and machine learning (ML) data analytics.…
Loopsy: Connecting Terminals and AI Agents Across Machines
Loopsy: Bridging Terminals and AI Agents Across Machines In the digital age, efficient data exchange and seamless communication between devices are paramount. L…
AI-Powered Anime Generation with SeeSee21/Z-Anime on Hugging Face
AI Powered Anime Generation with SeeSee21/Z Anime on Hugging Face Artificial intelligence continues to redefine the creative landscape, and one notable innovati…
AI Tool: GitHub's New AI-Powered Code Assistant
AI Tool: GitHub's New AI Powered Code Assistant GitHub has recently equipped developers with a revolutionary AI powered code assistant, which can produce, debug…
AI Tool: GitHub's ad-si for Enhanced Coding Assistance
GitHub's ad si: Revolutionary Coding Assistance In the rapidly evolving tech landscape, GitHub's ad si emerges as a powerful AI tool designed to significantly e…
Trading System V2: AI's Role in Deterministic Execution
Thanks to the incredible feedback on my last post, I’m officially moving away from the "distributed veto" system (where 8 LLM agents argue until they agree to trade). For v2, I am implementing a strict State Machine using a deterministic runtime (llm-nano-vm). The new rule is simple: Python owns the math and the execution contract. The LLM only interprets the context. I've sketched out a 5-module architecture, but before I start coding the new Python feature extractors, I want to sanity-check the exact roles I’m giving to the AI. Here is the blueprint: 1. The HTF Agent (Higher Timeframe - D1/H4) Python: Extracts structural levels, BOS/CHoCH, and premium/discount zones. LLM Role: Reads this hard data to determine the institutional narrative and select the most relevant Draw on Liquidity (DOL). 2. The Structure Agent (H1) Python: Identifies all valid Order Blocks (OB) and Fair Value Gaps (FVG) with displacement. LLM Role: Selects the highest-probability Point of Interest (POI) based on the HTF Agent's narrative. 3. The Trigger Agent (M15/M5) 100% Python (NO LLM): Purely deterministic. It checks for liquidity sweeps and LTF CHoCH inside the selected POI. 4. The Context Agent LLM Role: Cross-references active killzones, news blackouts, and currency correlations to either greenlight or veto the setup. 5. The Risk Agent 100% Python (NO LLM): Calculates Entry, SL, TP, Expected Value (EV), and position sizing. The state machine will only transition to EXECUTING if the deterministic Trigger and Risk modules say yes. The LLMs are basically just "context providers" for the state machine. My questions for the quants/architects here: Does this division of labor make sense? Am I giving the LLMs too much or too little responsibility in step 1 and 2? By making the Trigger layer (M15/M5) 100% deterministic, am I losing the core advantage of having an AI, or is this the standard way to avoid execution paralysis? Would you merge the HTF and Structure agents to reduce token constraints/hallucinations, or is separating them better for debugging? Would love to hear your thoughts before I dive into the codebase.
Manoj Mallick's AI Tool on GitHub: A New Hacker News Feature
Manoj Mallick's AI Tool on GitHub: A Revolution in Hacker News Manoj Mallick, a prolific developer, has introduced a groundbreaking AI tool on GitHub, making wa…
AI Tool: Few-Shot Learning with GitHub's Few-Sh
AI Tool: Few Shot Learning with GitHub's Few Shot Learning Library Few Shot learning is a transformative approach within the artificial intelligence (AI) domain…
Arc Gate: Advanced Prompt Injection Protection for OpenAI
Built Arc Gate — sits in front of any OpenAI-compatible endpoint and blocks prompt injection before it reaches your model. Try it here — no signup, no code, no setup: https://web-production-6e47f.up.railway.app/try Type any prompt and see if it gets blocked or passes. The examples on the page show the difference. The main detection layer is a behavioral SVM on sentence-transformer embeddings — catches semantic intent, not just pattern matches. Phrase matching is just the fast first pass. Four layers total. Benchmarked on 40 OOD prompts (indirect, roleplay, hypothetical framings — the hard stuff): • Arc Gate: Recall 0.90, F1 0.947 • OpenAI Moderation: Recall 0.75, F1 0.86 • LlamaGuard 3 8B: Recall 0.55, F1 0.71 Zero false positives on benign prompts including security discussions and safe roleplay. Block latency 329ms. One URL change to integrate into your own project: base\_url=“https://web-production-6e47f.up.railway.app/v1” GitHub: github.com/9hannahnine-jpg/arc-gate — star if useful.
SenseNova-U1-8B-MoT: New AI Tool on Hugging Face
Discovering SenseNova U1 8B MoT: A New AI Tool on Hugging Face SenseNova's latest release, SenseNova U1 8B MoT, is making waves on Hugging Face, opening up a wo…
Open Bias: AI Bias Detection Tool on GitHub
Open Bias: AI Bias Detection Tool on GitHub Introduction AI has revolutionized numerous sectors with automated decisions cloaked in algorithms, but it's not imm…
Machine.dev: Revolutionizing AI Development with New Tool
Machine.dev: Paving the Way in AI Development Machine.dev has launched a groundbreaking tool to streamline AI development. This innovative suite of resources is…
Open Models Narrowing AI Performance Gap
a year ago there was a clear tier gap. now i'm less sure, but not in the way i expected. the tasks where open-weight models have genuinely caught up are real: coding assistance, summarization, instruction following, solid day-to-day reasoning. for probably 70-80% of what most people actually use these for, a well-quantized local model is competitive. that wasn't true 18 months ago. but the remaining gap is stubborn. deep multi-step reasoning, anything requiring broad factual accuracy across domains, novel problem synthesis under ambiguity. that stuff still feels like a generation behind. and the frustrating part is it's not a fixed target. every time open models close in, frontier moves. what i can't work out is whether that's sustainable long term. at some point the architecture matures and the gap collapses for good. or maybe compute access keeps the ceiling moving indefinitely. for those who actually run both regularly - is there a specific task category where you've genuinely tried to substitute an open model and just couldn't?
AI Tool FTAIP: Revolutionizing AI Development on GitHub
FTAIP: Revolutionizing AI Development on GitHub The world of Artificial Intelligence (AI) is rapidly evolving, and developers are constantly seeking tools that …
AI Optimists vs. Pessimists: Will AI Reduce Unemployment?
How does what Dario is saying that unemployment is going to 20% if AI is going to be used to solve our problems? AI is a tool for humans to point at problems and solve them. Making humans act less like machine. Good. Making humans afraid that they will lose their income source because of a machine. Bad. This doesn’t make logical sense. Do they not like humans and want to solve their problems? Unemployment is one of our biggest problems. And they are saying that AI can’t fix it? Also, universal job guantee polls higher than universal basic income. Most people like to work and provide value. They don’t like being exploited and living in fear that their livelihood will be erased. What am I missing here AI optimists? AI pessimist? Realists?
AI Agents: Identity, Not Memory, Was the Key to Stability
Everyone's building memory layers right now. Longer context, better embeddings, persistent state across sessions. I spent weeks on the same thing. But the failure mode that actually cost me the most debugging time had nothing to do with memory. Here's what it looked like: an agent would be technically correct - good reasoning, clean output - but operating from the wrong context entirely. Answering questions nobody asked. Taking actions outside its scope. Not hallucinating. Drifting. Like a competent person who walked into the wrong meeting and started contributing without realizing they're in the wrong room. I run 11 persistent agents locally. Each one is a domain specialist - its entire life is one thing. The mail agent's every session, every test, every bug fix is about routing messages. The standards auditor's whole existence is quality checks. They're not generic workers configured for a task. They've each accumulated dozens of sessions of operational history in their domain, and that history is what makes them good at their job. When they started drifting, my first instinct was what everyone's instinct is: better memory. More context. None of it helped. An agent with perfect recall of its last 50 sessions would still lose track of who it was in session 51. What actually fixed it I separated identity from memory entirely. Three files per agent: passport.json - who you are. Role, purpose, principles. Rarely changes. This is the anchor. local.json - what happened. Rolling session history, key learnings. Capped and trimmed when it fills up. observations.json - what you've noticed about the humans and agents you work with. Concrete stuff like "the git agent needs 2 retries on large diffs" or "quality audits overcorrect on technical claims." The agent writes these itself based on what actually happens. Identity loads first, then memory, then observations. That ordering matters. When the identity file loads first, the agent has a stable reference point before any history lands. The mail routing agent learned the sharpest version of this. When identity was ambiguous, it would route messages from the wrong sender. The fix wasn't better routing logic - it was: fail loud when identity is unclear. Wrong identity is worse than silence. The files alone weren't enough Three JSON files helped, but didn't scale past a few agents. What actually made 11 work is that none of them need to understand the full system. Hooks inject context automatically every session - project rules, branch instructions, current plan. One command reaches any agent. Memory auto-archives when it fills up. Plans keep work focused so agents don't carry their entire history in context. The system learned from failing. The agents communicate through a local email system - they send each other tasks, status updates, bug reports. One agent monitors all logs for errors. When it spots something, it emails the agent who owns that domain and wakes them up to investigate. The agents fix each other. The memory agent iterated three sessions to fix a single rollover boundary condition - each time it shipped, observed a new edge case, and improved. These aren't cold modules. They break, they help each other fix it, they get better. That's how the system got to where it is. You don't need 11 agents The 11 agents in my setup maintain the framework itself. That's the reference implementation. But u could start with one agent on a side project - just identity and memory, pick up where u left off tomorrow. Need a team? Add a backend agent, a frontend agent, a design researcher. Three agents, same pattern, same commands. Or scale to 30 for a bigger system. Each new agent is one command and the same structure. What this doesn't solve This all runs locally on one machine. I don't know whether identity drift looks the same in hosted environments. If u run stateless agents behind an API, the problem might not exist for you. Small project, small community, growing. The pattern itself is small enough to steal - three JSON files and a convention. But the system that keeps agents coherent at scale is where the real work went. pip install aipass and two commands to get a working agent. The .trinity/ directory is the identity layer. Has anyone else tried separating identity from memory in their agent setups? Curious whether the ordering matters in other architectures, or if it's just an artifact of how this system evolved.
AI's Productivity Boost: Layoffs or Worker Benefits?
I keep hearing that AI will make workers more productive. But the part I don’t understand is this: If one employee can now do the work of three people, why is the default outcome usually: * fire two people * keep the same workload * give the remaining person more pressure * send the savings upward Why isn’t the obvious outcome: * shorter work weeks * higher wages * lower prices * more time off * better services It feels like AI is being sold to the public as “everyone will be more productive,” but implemented by companies as “we need fewer humans.” Maybe I’m missing something, but productivity gains only feel like progress if normal people share in them. Otherwise it’s not really “*AI helping workers*.” It’s just automation being used as a layoff machine. **Do you think AI will actually improve life for workers, or will it mostly just increase profits while making jobs more insecure?**