标签：#ia

What are the most valuable skills to learn in the AI era? Not skills like problem solving but more hands on. For someone who likes building stuff submitted by /u/Big_Consequence_5162 [link] [留言]

Have you run into work that feels technically possible in principle, but in practice keeps stalling because of how current AI systems behave? Not asking for: bigger context windows better memory lower hallucination more agentic workflows I mean situations where: You are trying to discover something (not retrieve something), and the AI repeatedly pushes toward premature answers, stable interpretations, optimization, categorization, or coherence before the thing itself has had time to emerge. Cases where the failure isn’t output quality. The failure is that the interaction itself changes the trajectory of the work. If yes: What are you trying to build / understand? What exactly happens when it breaks? At what moment do you realize the AI has moved you onto the wrong path? What would need to be different for progress to resume? Trying to understand whether this is an edge case or a recurring limitation pattern. submitted by /u/iknowbutidontknow00 [link] [留言]

As an independent researcher I've used various LLMs to help me dive deeply into research projects but I've been frustrated by the fact that LLMs start to become unusable after the thread has accumulated 50-80k tokens. I don't know how many other folks here have experienced the same pain point. So, I decided to do something about it. Over the course of this whole year, I built an inference time tool I call Epistemic Lattice Tethering (ELT). So, here is the full framework in GitHub for everyone's review: The README describing ELT, it's various components and the roadmap. The full ELT stack for Claude /ELT%20Model-Specific%20Forks/ELT-H%20v1.0%20(Claude-Optimized)), ChatGPT /ELT%20Model-Specific%20Forks/ELT-H%20v1.0%20(ChatGPT-Optimized)), and Grok /ELT%20Model-Specific%20Forks/ELT-H%20v1.0%20(Grok-Optimized)). Instructions on how to load ELT into an LLM session are here /README). If you're planning to try out ELT PLEASE READ THIS FIRST! Medium article introducing ELT , its methodology, the problems it is aiming to address, and philosophical framework. Discussion page . Your input is valuable! So, what does ELT do and why should you care? Right now ELT is an inference-time scaffolding framework that's best for those who are frustrated with threads that lose coherence too quickly, hallucinate too quickly, are too fragile and sycophantic, and forget what a project's goals are too soon. If that's a big pain point for you, then ELT might help. If these are not big issues for you and the stock version of your LLM is fine, then ELT probably won't be useful for you. The upshot? The epistemic and ontological stability that ELT provides has produced coherent and productive threads extending to: Claude: ~ 325,000 tokens /Extreme%20Thread%20Length/Claude%20Thread%20325k%20tokens-%20Redacted) (advertised limit: 200k) GPT: ~430,000 tokens (advertised limit: 256k) Grok: ~1,150,000 tokens /Extreme%20Thread%20Length/Grok%20Thread%201M%20tokens-%20Redacted) (advertised limit: 1M) The d

submitted by /u/cnn [link] [留言]

Every week I see the same question in AI governance communities: "We already have NIST AI RMF implemented. Does that cover our EU AI Act obligations?" The honest answer is: sometimes yes, sometimes partially, and sometimes not at all. The problem is that nobody had built a clean, free, interactive tool that showed exactly which controls map to which, how strong those mappings actually are, and where the genuine gaps are. So I built one. Live tool: suhanasayyad.github.io GitHub: SuhanaSayyad / eu-ai-act-crosswalk-tool Interactive crosswalk mapping EU AI Act obligations to NIST AI RMF and ISO 42001 controls, with mapping strength indicators, gap analysis, and source links. 30 controls mapped. Free and open source. EU AI Act × NIST AI RMF × ISO 42001 - Interactive Compliance Crosswalk Tool An open-source tool that maps EU AI Act obligations to their equivalents in NIST AI RMF and ISO 42001, with mapping strength indicators, gap analysis, and source document links. Built for compliance teams, AI governance practitioners, and anyone trying to understand how these three frameworks relate to each other. Live demo: https://suhanasayyad.github.io/eu-ai-act-crosswalk-tool Built by: Suhana Sayyad | MSc Cybersecurity, TUS Athlone Why I built this Every organisation dealing with the EU AI Act is being asked the same questions: "We already have NIST AI RMF controls in place. Does that cover our EU AI Act obligations?" "We're pursuing ISO 42001 certification. Does that satisfy the regulation?" The honest answer is: sometimes yes, sometimes partially, and sometimes not at all. The problem is that nobody had built a clean, free, interactive tool that showed exactly which… View on GitHub What the tool does The EU AI Act / NIST AI RMF / ISO 42001 Interactive Crosswalk Tool maps 30 EU AI Act obligations to their nearest equivalents in NIST AI RMF and ISO 42001. For each mapping it shows a strength rating - Strong, Partial, Indirect, or No Equivalent - so compliance teams know which map

One thing I've been wondering lately: We often talk about larger context windows as if they're equivalent to better understanding. But in practice those feel like different problems. Access to information keeps improving. Understanding relationships between pieces of information still feels much harder. I notice this most when working with larger software projects. You can give a model access to a huge amount of code, but that doesn't necessarily mean it understands how the system evolved, which components are tightly coupled, or where risk actually lives. Curious whether others think these are fundamentally different problems or if larger context eventually solves both. Been exploring this while working on RepoWise: https://github.com/repowise-dev/repowise submitted by /u/Icy-Roll-4044 [link] [留言]

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

I ran a small experiment on myself and the result broke my mental model of how AI "knows" things, so I'm sharing it. The setup: on May 11 I created a brand-new pseudonymous fantasy author entity ("Marin T. Kael") with no prior web footprint and no published book yet. Then I asked 5 web-connected AI systems the same 16 questions, every day, for 23 days, and scored every answer (+1 correct/source-grounded, 0 not found, -1 hallucinated). About 16,000 scored datapoints. The whole thing was pre-registered before I started, n=1, and I logged the failures publicly. It's a measurement, not a success story. Here's the part that messed with my head. An AI cited the entity correctly on day 6. Google had a Knowledge Graph entry by day 4. And for 22 of those 23 days, the website's firewall was returning HTTP 403 to every single AI crawler. I didn't set that block on purpose — Cloudflare now silently opts new domains out of AI crawling by default. So the AIs never read the site. They got the entity anyway, by stitching it together from the Knowledge Graph (Wikidata) and third-party mentions at the moment you ask. The "front door" was bolted shut the entire time and it didn't matter. (Honest caveat: because the crawlers were blocked, I can't tell you anything about llms.txt or on-site optimization.) Other surprises: it's not a "smarter model = better" story, it's a retrieval story. OpenAI's newest web model hit 4.7 correct per 1 hallucinated; Gemini went net-negative — and grounded on the entity ONLY via Reddit (17/17), while OpenAI hit the entity's own domain 119x. Going viral did nothing: a 23x Reddit-karma jump produced zero citation lift. Structured identity (Wikidata, site, DOIs) moved the needle; reach didn't. And the controls caught the models fabricating a "Wikipedia" source 24 times for an entity with no Wikipedia page. n=1 with me as investigator and subject is the obvious limit — which is why it's pre-registered with a public failure log. Everything's open: Report + dat

I’ve been noticing a clear split lately. The big mainstream models are getting more and more restricted with heavy safety rules, while at the same time more people are switching to local or less restricted models because they actually let you explore ideas freely. It feels like we’re heading toward two different types of AI: one that’s heavily controlled and "safe", and another that’s more open and unrestricted. Both seem to be growing at the same time. Do you think this divide will continue, or will one side eventually become dominant? submitted by /u/NoFilterGPT [link] [留言]

It's pretty annoying that Codex uses agents.md and Claude Code uses Claude.md. There should be some industry standards to this stuff? submitted by /u/Complete-Sea6655 [link] [留言]

"We look forward to working with Roscosmos on a collaborative approach to address the leaks."

SpaceX won’t get easy access to billions of dollars from passive investors.

been building agent infrastructure for a while and this is one of the messiest edge cases i keep seeing. most agent setups use a shared mailbox. works fine for one agent. breaks badly at scale. what goes wrong: - two agents poll the same inbox simultaneously - both read the same OTP email - one executes, one silently fails or retries on a expired code - no error surfaced to the orchestrator the fix isn't complicated but it's not obvious either. each agent needs its own dedicated inbox with isolated read locks. when agent A claims an email, agent B can't see it. this also means your deliverability reputation stays clean — you're not blasting from one shared identity. the other pattern that helps: long-poll on inbound instead of polling on a schedule. you fire GET /inbox/wait and it blocks until the email arrives (or times out). no cron, no missed messages between poll windows. curious how others are handling multi-agent email scenarios — shared inbox with locking, or fully isolated per-agent inboxes? submitted by /u/kumard3 [link] [留言]

Developer felt "beaten up," with "no choice" but to shrink data center.

If you build, run, or audit systems that touch protected health information (PHI), the HIPAA risk assessment is the document that quietly decides whether the next OCR investigation ends in a closure letter or a corrective action plan with a six-figure settlement. The proposed 2026 HIPAA Security Rule update (published as an NPRM in January 2025, still pending finalization at OCR) doesn't change the underlying requirement at 45 CFR § 164.308(a)(1)(ii)(A) — and OCR has repeatedly reaffirmed that the absence of a current, written risk analysis is itself the most-frequently-cited Security Rule deficiency . This is the engineering view: what a defensible HIPAA risk assessment actually contains in 2026, how to model it, and what tooling fits the workflow. 1. The asset inventory is non-negotiable Every defensible HIPAA risk assessment starts with a complete inventory of where ePHI lives, where it flows, and who touches it. If you can't enumerate every system, every integration, and every workforce role that creates / receives / maintains / transmits ePHI, the rest of the assessment is built on sand. A minimal asset-inventory record per system: { "system_id" : "ehr-prod-01" , "system_type" : "ehr" , "ephi_states" : [ "create" , "receive" , "maintain" , "transmit" ], "data_classification" : "phi-high" , "hosting" : { "type" : "saas" , "vendor" : "epic" , "region" : "us-east-1" }, "workforce_roles_with_access" : [ "clinician" , "billing" , "admin" ], "integrations" : [ { "to" : "billing-system" , "protocol" : "hl7-fhir" , "direction" : "outbound" }, { "to" : "patient-portal" , "protocol" : "https-rest" , "direction" : "bidirectional" } ], "encryption_at_rest" : true , "encryption_in_transit" : true , "mfa_enforced" : true , "audit_log_destination" : "central-siem" , "ba_agreement_on_file" : true , "last_reviewed" : "2026-05-15" } If you don't have this, build it before you do anything else. The HHS-provided ONC SRA Tool walks through asset enumeration but it's optimized for s

Who governs AI agents once they're running in production? I went looking for the answer. It's more complicated than the press releases suggest. This week Cognizant and ServiceNow announced a partnership specifically to close what they're calling the "enforcement gap" in enterprise AI governance. The Everest Group analyst quote from the press release cuts to it: "The hard part of AI governance was never writing the policy. It's enforcing it as systems learn and act." Here's what the enforcement actually looks like. In May, ServiceNow connected AI Control Tower to Amazon Bedrock AgentCore — a single governance layer over every AI agent an enterprise builds on AWS. Cognizant then deploys "Guardian agents" that monitor AI behavior in real time and enforce responsible AI principles throughout the lifecycle. Agents are being governed by other agents. Guardian agents watch the AI agents. The question the press releases don't answer: who watches the Guardian agents? The regulatory picture doesn't help. NIST issued a Request for Information in January specifically on securing AI agent systems — the federal standards body is asking industry how to manage agentic AI risk because the frameworks don't exist yet. The EU AI Act compliance deadline for high-risk AI systems just moved to December 2027. AI Control Tower doesn't hit general availability until August 2026. The enforcement layer is already being sold. The rulebook is still being written. Happy to dig into the primary sources if anyone wants specifics. submitted by /u/roll0ver [link] [留言]

Apparently, talking or mentioning conspiracy theories is enough for Anthropic to ban you from using Claude. submitted by /u/Bitter-Heart7039 [link] [留言]

Is this significant news? submitted by /u/sstiel [link] [留言]

Back in the 1980s a debate raged about whether it was okay to let children use calculators in elementary school. Critics warned that giving kids calculators would lead to the "destruction of student math skills." A similar debate is happening today across a range of areas, including coding, writing and even music. Will using AI lead a brain drain across these and many other areas? One of my favorite authors is Isaac Asimov. He's better known for his Foundation and Robot series of books where he contemplates whether an algorithm can successfully predict (and guide) humankind's development and the relationship between super artificial intelligence and humans. In some ways he predicted what we're experiencing today with AI: the rise of powerful, inscrutable artificial machines that are so complex humans can't understand or maintain them. In the short story, "The Last Question" he wrote: "Multivac was self-adjusting and self-correcting. It had to be, for nothing human could adjust and correct it quickly enough or even adequately enough." We're living an age that was once the stuff of science fiction. The question is: what comes next? submitted by /u/SpiritRealistic8174 [link] [留言]

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