标签：#ci

submitted by /u/NISMO1968 [link] [留言]

Researchers have prototyped an AI-powered internet worm . The coolest thing about the prototype is that it carries its own LLM with it, and runs it on computers that have been broken into. This is the closest to John Brunner’s original 1975 conception of a computer worm that I’ve seen.

submitted by /u/Quirky_Inevitable630 [link] [留言]

We've been running AI across a team for about two years. Expected the hard parts to be the models. They weren't. The problem that cost us most early on was context. We had a system making customer-facing recommendations without access to the business-specific knowledge it needed to answer accurately. Spent too long trying to fix it at the prompt level. The context layer didn't exist, and prompting didn't fill that gap, it just made it less obvious until something downstream failed badly enough to trace back to it. That failure pushed us to map the other places where AI builds break structurally rather than technically. We found five more, and they kept showing up across different stacks and different team sizes in roughly the same order. The first is identity, when you move from one person's AI to a team's AI, shared context without role-based permissions either creates noise or recreates the same knowledge silos you were trying to escape. The second is decision memory, records of what was decided aren't the same as memory of why, and that gap compounds quietly until a new team member gets a confident wrong answer from a system referencing reasoning that was abandoned months ago. The third is attention. Dashboards only work if someone looks at them, and the failure mode of every dashboard ever built is the same: critical things slip through when life gets busy. The fourth is write-back. Manual logging is a tax on the busiest moments, and the more important the work, the less likely anyone stops to document it. The fifth is governance, when the same agent that builds something also evaluates it, that's not a check, it's a loop grading its own homework. The sixth is economics, at solo scale AI cost is a rounding error, at team scale you're looking at a vendor invoice with no way to connect spend to specific workflows or outcomes. Which of these have you hit? And did they show up in this order or did something else surface first? If you're interested, we turned these i

LinkedIn’s Karthik Ramgopal and Prince Valluri discuss leveraging AI as a new execution model for large-scale engineering. They explain how to move beyond fragmented implementations by building platform abstractions for orchestration, structured context, and safe tooling like MCP. They share architectural insights from real-world coding, observation, and UI testing agents built at LinkedIn. By Karthik Ramgopal, Prince Valluri

Dropbox has unveiled Nova, an internal platform designed to orchestrate and operationalize AI coding agents across the company's engineering workflows. By Craig Risi

I kept shipping AI apps with no idea what was happening under the hood — prompts going in, responses coming out, costs creeping up, and zero visibility into any of it. So I built LogLens. Add one line of code and it logs every single AI call your app makes — the full prompt, completion, latency, token count, and cost — all in a clean dashboard. Works with Anthropic and OpenAI out of the box. No framework lock-in. npm install loglens const anthropic = wrapAnthropic(new Anthropic(), { apiKey: 'your-key' }) // that's it — every call is now logged Built the whole thing in ~48 hours using Claude Code. Still early but fully working. Free early access here: llm-watch.vercel.app Would love feedback — what features would make you actually use this day to day? submitted by /u/ProcessAutomatic6941 [link] [留言]

“Why wouldn’t you want to be in both Pepsi and Coke?” says one venture capitalist. “It’s the same here.”

Most powerful AI/agent tools nobody talks about, and it leaves you behind IMO 1. Instructor define a Pydantic model, get clean structured JSON out of any LLM every time → https://github.com/567-labs/instructor 2. Octopoda gives any AI agent persistent memory and catches it when it loops and quietly burns your tokens. open source → https://www.octopodas.com 3. E2B secure cloud sandboxes so your agent can actually run the code it writes without nuking your machine → https://e2b.dev 4. Firecrawl turn any website into clean, LLM-ready markdown in one API call → https://firecrawl.dev 5. Composio plug your agent into 1000+ apps (Gmail, Slack, GitHub) with the auth handled for you → https://composio.dev 6. LiteLLM one API for 100+ models across OpenAI, Anthropic and local, swap without rewriting a line → https://github.com/BerriAI/litellm what are yours, let me know and I will add it to the list next month! submitted by /u/DetectiveMindless652 [link] [留言]

An “urban explorer” tells WIRED their mother checked in to make sure they weren’t one of the people seen scurrying out of a manhole with their friends.

On June 5, 404 Media reported that attackers had been using Meta’s AI customer support agent to steal Instagram accounts. Their approach was simple: They asked the agent to link the accounts to email addresses that they controlled, and the agent complied. One attacker broke into the dormant Obama White House account and made pro-Iran…

AI companies are using serif to project humanity. Critics are calling it “tasteslop.”

This series provides your roadmap for the machine age, exploring how to move from vulnerable prototypes to resilient systems through layered defense, robust MLOps, and integrated governance. By Claudio Masolo

so anthropic just dropped a blog post calling for a global pause on frontier ai development, warning that models could start recursively self-improving and spiral beyond human control. sounds scary. sounds noble. let's talk about what's actually going on here. anthropic is reportedly eyeing a $1 trillion+ ipo, and they just happen to be the ones calling for everyone to stop building. analysts are already asking whether this is really just about freezing the status quo so they can hold their lead. putting it plainly: a pause helps anthropic keep its position and probably grow market share too. and here's where it gets a bit hypocritacal: over 80% of the code in anthropic's own codebase is now written by claude. they're absolutely running the playbook they want everyone else to put down. but the thing nobody's really talking about is regulatory capture. this is textbook. you become the dominant player, go to governments, say "this technology is dangerous, we need oversight, we're the responsible ones, let us help write the rules." suddenly the regulations that get passed only you can afford to comply with, locking in your architecture, your safety benchmarks, your evaluations. smaller competitors get crushed under compliance costs, open source gets kneecapped, and you get a moat that no vc cheque can cross. they compared it to nuclear arms control which sounds serious until you realise ai training is far easier to hide than a missile silo, so any agreement just punishes the people honest enough to follow it. the safety concerns might be real. but the timing, the ipo, the regulatory push is all hard to look at all that and not raise an eyebrow. submitted by /u/Complete-Sea6655 [link] [留言]

We're opening up Creaibo 2.0 beta applications, and I'd genuinely love to get feedback from this community. What is Creaibo? An AI-powered creative tool for images, video, and content production. We're focused on giving creators a more coherent workflow rather than yet another single-task generator. Cora is our core AI assistant inside the product. Why post here? Because people here actually use these tools seriously and have real opinions. We've been building based on the frustration that AI tools are great at individual tasks but terrible at keeping your creative context together across a project. Curious if that resonates. What we're looking for in beta testers: Anyone actively creating content with AI, whether that's video, images, marketing assets, or anything in between. Especially useful: people willing to tell us what's broken. Apply here: https://www.creaibo.com/survery We also published a new Cora demo this week if you want to see what the tool actually does before applying: https://www.bilibili.com/video/BV1ETEF6VEHu/ Happy to answer questions in the comments. submitted by /u/Objective_Dirt_9799 [link] [留言]

found this buried in the openai dashboard and honestly surprised more people don’t know about it it’s called the data sharing program. go to your api dashboard, hit data controls, toggle on sharing. that’s it. you get free tokens every single day. up to 2.5 million tokens daily on the lighter models like gpt-4o-mini, o3-mini, gpt-4.1-mini. for the heavier models it’s 250k tokens per day. resets daily. the trade is your prompts and outputs can be used by openai to train their models. so don’t use it for client work or anything sensitive but for side projects, learning, experiments… you’re basically getting free api access every day just for flipping a toggle not a trial. not a promo. it’s an ongoing program and it just sits there unclaimed for most people submitted by /u/NewMuffin3926 [link] [留言]

The CMA's conduct requirement under the UK Digital Markets, Competition and Consumers Act is the first binding law to separate content display rights from AI training data rights at domain and page level, covering Google AI Overviews, AI Mode, Gemini, and Vertex AI simultaneously, with a phased implementation calendar: main publisher controls by December 2026 and page-level grounding controls by March 2027. CMA chief Sarah Cardell explicitly signaled additional Google search requirements in coming weeks, and the CMA's biannual public compliance reporting obligation gives it a fast-acting mechanism if Google stalls. An anti-retaliation clause bars Google from penalizing opt-out publishers in organic rankings, closing the coercion mechanism that has made voluntary consent frameworks unworkable since AI Overviews launched in the UK in late 2025, when zero-click searches rose roughly 30% in health and local news categories. Fair licensing terms were explicitly deferred to a separate proceeding, a gap publisher trade bodies have already criticized and one the CMA has already signaled it intends to fill in its next enforcement phase. More : https://aiweekly.co/alerts/cma-orders-google-ai-search-opt-out-for-publishers submitted by /u/Justgototheeffinmoon [link] [留言]

submitted by /u/ItsGazH [link] [留言]

I recently analysed UK occupation data to see which job categories appear most exposed to current-generation AI systems. The results are probably not what most people here would predict. Using ONS workforce data mapped to ISCO-08 occupation groups, I assigned AI exposure scores based on how much of an occupation's core task bundle can already be completed or substantially augmented by current models and automation systems. The highest score was not software development. It was clerical support work. Clerical occupations scored 8.5/10 across roughly 3 million UK workers. This includes administrative assistants, receptionists, customer service representatives, data-entry workers, call-centre staff, and bookkeeping clerks. The reason becomes obvious when you break occupations into tasks. Modern LLMs are exceptionally good at: Information retrieval Structured communication Summarisation Classification Form completion Draft generation Customer interaction workflows Those capabilities overlap directly with a large percentage of clerical work. Professionals scored 6.5/10. That category includes lawyers, engineers, accountants, analysts, architects, and software developers. What's interesting is that exposure and displacement aren't the same thing. A lawyer using AI to draft contracts becomes more productive. A customer-support department replacing a large portion of repetitive ticket handling with AI may reduce headcount entirely. The underlying capability overlap can be similar while labour-market outcomes are very different. The lowest-risk categories remain occupations requiring physical adaptation to unpredictable environments. Trades and elementary occupations scored between 2.0 and 2.5. One takeaway is that AI discussion often focuses on whether models can write code. The labour-market impact may arrive first through administrative and support functions because those workflows are already highly structured and relatively easy to automate. Curious how others here woul

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