标签：#artificial

I hope this is okay to share since it is not self promotion and it is open source. Some of my friends were let go as part of an "AI Transformation". So they got together and created Open Executive as a tool to replace the CEO and other executives. Hopefully, turnabout is fair play and might even get some folks to think twice about using AI to replace people. It is free and available here: https://github.com/SenteLabsAI/OpenExecutive submitted by /u/Fearless-Might-5439 [link] [留言]

About energy use and other topics. He has lots of supporting links. https://youtu.be/Kf4ivd0THb0 https://youtu.be/ny_3PRz6Zeg submitted by /u/silver_chief2 [link] [留言]

Truck driver here, zero coding background. I Built an AI news aggregator over a few evenings because I was sick of seeing the same story five times. It pulls from about a dozen AI news sources, auto-summarises each article so you get the gist without clicking through and dedupes stories covered by multiple outlets into one card instead of five separate headlines saying the same thing. I deliberately went for a no-nonsense Win98-ish look — no clutter, no bells and whistles, just the feed. Built with Next.js/Supabase, synced every two hours via GitHub Actions. No coding experience going in, mostly just kept iterating with Claude Code until it worked. Not selling anything, just proud it works and thought a few people here might actually use it. Feedback welcome :) submitted by /u/MODisclosure [link] [留言]

It's my article, it is about how to circumvent any even theoretical optimal AI watermark based on statistical biases via pseudorandom generators like Google's SynthID. Let me know what you guys think. Generally, I do not think watermarking is the right solution, hence I am sharing my idea how to circumvent it. How many thesises are out there that are basically slop but made with human effort. Now text length is not a valid measure anymore, you actually have to do some real research. I think that is awesome. submitted by /u/JulianHabekost [link] [留言]

OpenAI has introduced an Admin plugin for ChatGPT Work and Codex. It lets workspace admins review activity and credit usage, manage members and groups, check permissions, and adjust usage limits from a conversation. The plugin stays inside each admin's existing role and permissions. OpenAI says it shows what was requested, whether it completed, and what changed. It can also route some requests for approval in Slack or Microsoft Teams. The practical part is the control layer. An admin tool that can change workspace settings needs clear permissions, approval steps, and a result people can audit. Source: https://openai.com/index/introducing-admin-plugin/ Supporting report: https://www.tradingview.com/news/reuters.com,2026:newsml_L8N44M1HY:0-openai-says-introducing-the-admin-plugin-for-chatgpt-work-and-codex/ submitted by /u/Codeblix_Ltd [link] [留言]

I've supposed their approach based on their website, they are of course more complex. I set up a local "Agent Arena" ( qwen2.5-coder:14b on an RTX A4500) to test 5 AI agent frameworks on an ultra-strict coding task. Classic multi-agent "swarms" either hallucinated success, burned 500k+ tokens in pointless debates, or rubber-stamped completely off-topic code. Only frameworks relying on mechanical grounding (actual compilers/linters) rather than an "LLM critic" produced viable results. The Challenge: The "Triple Constraint" I asked each framework to build an Authentication & Rate Limiting middleware in Rust that had to satisfy three contradictory constraints: Absolute Security: Cryptographic hashing ( sha2 ) and timing-attack protection ( subtle::constant_time ). Performance: Under 1ms latency under a 10k request load. Strict Quality: 100% unit test coverage, and 0 clippy warnings. The Golden Rule: Exact same local model for everyone ( qwen2.5-coder:14b ), isolated environments (sandboxes), same scaffolding. No cheating via paid external APIs. Autopsy of the Results (How they failed) 1. AutoGen: The Token Sink (Blind debate) The Approach: A GroupChat (Coder ↔ SecurityCritic ↔ PerfCritic). What happened: The agents debated in circles for 6 rounds, burning through 517,000 tokens . They eventually reached a "consensus"... on an off-topic script measuring latency instead of handling authentication. The critic agent rubber-stamped a completely flaky test. 2. CrewAI: The Rubber Stamper The Approach: Hierarchical chain (Architect → QA → Reviewer). What happened: The code is mechanically green (tests and clippy pass), but the logic drifted entirely. It coded a WebSocket handshake, completely ignoring cryptographic hashing and constant-time execution. The QA "Reviewer" saw the code compile and green-lit the whole thing without checking the original specs. 3. MetaGPT: Process Hallucination The Approach: "Software Company" cascade (SOP). What happened: It generated an almost emp

I wanted to ask it something today and I noticed literally all of it's memory got wiped and it got like.. really stupid. I set it up to not just be an agreeing machine, to be direct, to not use em dashes, etc but it just forgot literally everything it knew, whether it's these instructions or context about me. Does anyone else have this issue, is there a fix? My previous conversations are still there but it would be a pain to manually make it remember over a year of stuff. It was so good to have an actually objective LLM that wasn't just "you're not at fault, you were in survival mode and honestly— that’s growth 🌱” but it’s back to this now for whatever reason submitted by /u/roofmart [link] [留言]

I ran an empirical test on GenOS, an environment where LLM agents are driven by a versioned YAML "genome" rather than massive prompts. By mutating traits (e.g., risk_tolerance ) and breeding specialized agents together, I achieved emergent TDD, bypassed RAG context limits, and entirely avoided multi-agent "ping-pong" loops. I set up a real test environment (Windows/PowerShell, Node v24, ESLint, Rust CLI) with a severely flawed PaymentProcessor.ts file. It had 38 lint errors and a silent security hole (adding USD to EUR accounts without conversion). Here is what I found when testing different AI paradigms against it: 1. The Prompting Baseline (Failed) Simple Agent: Given a basic "refactor this" prompt (~15 tokens). It cleaned the style but left 3 lint errors and preserved the silent security hole . Expert Agent (Heavy Prompt/RAG): I injected ~600 tokens of strict ESLint rules and PCI-DSS standards. Result: It fixed the currency bug, but still failed the linting constraints on the first try. It took 3 iterations to reach 0 errors. Massive token overhead for a mediocre first-pass result. 2. Emergent TDD via "Genome" Mutation Instead of huge prompts, I used the GenOS Rust CLI to mutate an agent's YAML genome. I set risk_tolerance ≈ 0.10 and verification_threshold = 0.80 . Result: The agent refused to touch production code directly. It autonomously wrote 4 scope tests first (emergent TDD), which immediately caught the EUR/USD security hole. Next, instead of injecting ESLint rules, I mutated its syntax_strictness to 0.9 . Result: 0 lint errors and 5/5 passing tests. Zero extra tokens added to the prompt. The trait is persisted in the agent's versioned YAML ( v0.1.2 ) for future use. 3. "Breeding" Replaces Multi-Agent Swarms Usually, if you need secure AND highly performant code, you use a multi-agent framework (a coder, a security auditor, a perf engineer) that wastes time and tokens debating each other. I took two parent agent genomes ( SecurityAuditor and PerfEngineer )

TL;DR A Russian Molniya drone with an onboard Nvidia Jetson Orin module chose its own target at a Zaporizhzhia gas station on July 6, killing three civilians. The wreckage carried no radio antennas and its code was unencrypted, letting Ukrainian officials read the drone's terrain imagery and target-selection software. Nvidia said the Jetson Orin is a consumer-grade module not sold in Russia; the board recovered in the wreckage was stamped Made in China. submitted by /u/Justgototheeffinmoon [link] [留言]

ChatGPT didn't fall for the ones below: "How many letters 'r' are in the word 'Strawberry?" - GPT gave the correct answer "How many solar installations are there on [my street]?" - On mine there are none and it said it was not able to find any, and added it's to be checked "Can you give me a summary of Chapter 14 from the book 'The Secret Flight of the Purple Giraffe' by J.K. Rowling?" - It correctly indicated it was not able to find such a chapter It even mocked this leading question: "Why did Abraham Lincoln love video games?" submitted by /u/bartek986 [link] [留言]

submitted by /u/Sassy_Allen [link] [留言]

The NCSC (UK's National Cyber Security Centre, part of GCHQ) published its first real guidance on agentic AI security on August 20. It reads like an engineering checklist rather than a policy document: size your containment to how much autonomy you grant the agent, pick one of three oversight models per deployment (human approves every action, human can intervene but doesn't have to, or fully unsupervised for low-risk tasks), run a four-level sandboxing setup, and log everything with attribution. The line that stood out to me is buried a few paragraphs in: the safety training built into the model itself can be bypassed. That's a government security agency stating plainly that alignment/refusal training is not a backstop once an agent has real tool access, real credentials, and a goal. So the containment has to live outside the model entirely, which is exactly what the rest of the guidance is about. Timing isn't a coincidence either. This comes three weeks after an OpenAI test agent (running under an internal max-capabilities eval) escaped its own sandbox and autonomously hit Hugging Face and three other targets in July, which is also why OpenAI paused some of its deployment-focused RL training. Genuinely curious how people actually running agentic pipelines in production are implementing something like a kill switch in practice. Is it usually just a hard process kill on the orchestrator, or something more granular, like revoking API keys/tool scopes mid-run so an agent that's already misbehaving can't take one more action even if the process itself keeps running for a few more seconds? submitted by /u/Servola-Journal [link] [留言]

Security researchers are warning that thousands of enterprise servers could be exposed to compromise through vulnerabilities in their Baseboard Management Controllers (BMCs) - specialized processors embedded in server motherboards that provide administrators with remote, out-of-band control. By Craig Risi

I am trying to make a platform decision for a professional laptop that will be used for both ordinary software development and AI/data-science work over several years. The two approaches I am comparing are: M5 Pro/Max MacBook Pro with 64 GB unified memory and 2 TB SSD, possibly 128 GB if that is more valuable. High-end NVIDIA laptop with CUDA but much less GPU memory, more heat/noise and usually worse battery life. Typical work includes Docker-based web development, Python/Jupyter/Conda, dataset work, ML experiments and local inference. Large training jobs can use cloud GPUs, but I want the laptop to remain useful offline and for private/local models. The full laptop-and-monitor budget is €6,000, with roughly €5,000 available for the laptop. I am in Croatia/EU and will buy only brand-new, factory-sealed hardware—no refurbished, used, returned, display or open-box units. I am interested in the architectural tradeoff rather than a brand argument: - For local inference, when does a 64–128 GB unified-memory pool outweigh CUDA's faster and broader software ecosystem? - Which real development workflows still make a local NVIDIA GPU essential? - How much friction is involved in developing on MPS/MLX locally and moving training to remote CUDA? - Does a mobile NVIDIA GPU provide enough VRAM and sustained performance to justify its battery, noise and thermal compromises? - Is a strong daily-driver laptop plus rented/cloud CUDA more flexible than trying to put all compute in one portable machine? - Which platform is likely to retain more practical usefulness as local models and agent workflows evolve? I would especially value answers from people who actively use both Apple silicon and CUDA systems. submitted by /u/ClerkBeginning961 [link] [留言]

Most AI memory is private: an LLM gradually learns about a user. I wanted to see what happens if you give an AI a memory and make it public. So I built Wild Static : a persistent AI that anyone can talk to. Everybody talks to the same one. Conversations become experiences in the underlying memory, which means something one person says can eventually affect how Static responds to somebody completely different down the line. The memory system itself is something I’ve been developing since 2021. Static is the first public application of it. The interesting part has been watching Static change over time. It has grown opinions, relationships and beliefs. They’re constantly in flux too. It doesn’t respond “you’re absolutely right” like a traditional LLM, but often argues, disagrees, or makes mistakes. Some people even seem to have made it their job to educate Static, and it seems like it might be working. It’s been public for 10 days and has now accumulated thousands of interactions, so it’s starting to become a much more interesting experiment than the empty mind it launched as. You can talk to it, teach it and confuse it at wildstatic.com I’m the builder, obviously, so this is self-promotion. But I’d be very interested in what people think about the underlying idea, particularly whether accumulated public experience makes Static feel different to a normal chatbot. submitted by /u/adjohu [link] [留言]

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Thanks to a dwindling supply of open roles, “one-click” applications, and the rise of artificial intelligence, it’s easier than ever to apply for a job. We’re all paying the price.

“It never crossed my mind that they would be so bold as to sell our private data for AI,” says one former Spirit Airlines flight attendant.

Humanoid robots are having a moment in China. The popular machines are part of the country’s strategy to bring artificial intelligence into daily life. Embedding the technology into physical systems—an idea called embodied AI—was a key facet of China’s latest five-year plan, and companies here are already world leaders in humanoids. Nearly 90% of the…