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It’s a glorious day in Kirkland, Washington, an affluent Seattle suburb on the eastern shore of Lake Washington. The temperature is in the mid-80s, and the sky is incapable of being any more blue. The view from the Gates Ventures conference room overlooks the Carillon Point Marina, where a flotilla of expensive boats bob in…
Why are AI scientists so adamant that frontier model LLMs are NOT conscious or sentient? submitted by /u/DoublePassRadiator [link] [留言]
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]
First Draft takes care of the busywork of video editing.
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]
X has sent cease-and-desist letters to Nitter, the open source project behind privacy-friendly X frontends, demanding its instances and code repository be taken down over alleged scraping.
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
Instagram says the process can produce a first pass in under 10 seconds, potentially saving creators significant editing time while making video creation more approachable for people who don't have much experience with editing software.
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]
The social app founded by Bonobos co-founder Andy Dunn is expanding beyond events with new digital homes for groups to connect, organize, and make plans.
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]