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This is part eleven in a series about managing the growing pile of skills, scripts, and context that AI coding agents depend on. Part nine covered workflow assets and resumable procedures. Part ten introduced the improve pipeline that continuously curates your stash. Earlier parts addressed teams, distributed stashes, and community knowledge. Most automation with AI agents is reactive. You open a session, give the agent a task, wait for the result, close the session. The agent's clock runs when you run it. Task assets flip that model. A task is a YAML file in your stash that defines a workflow — what to run, when to run it, what environment it needs, and how long it's allowed to take. Once registered, the task runs on schedule without your involvement. The OS scheduler calls akm tasks run <id> , which executes the task and writes the result to state.db . You find out what happened when you check akm health or look at the log. This is the piece of akm 0.8.0 that makes continuous operation possible. The improve loop runs twice an hour because a task asset says it does. The hourly Discord health report fires because a task asset says it does. Neither requires an open terminal. The Task Asset Format Task assets live at <stash>/tasks/<id>.yml . The filename is the task ID. A minimal task looks like this: schedule : 0 * * * * command : akm improve --auto-accept 90 enabled : true That's enough to install a cron entry and run akm improve at the top of every hour. The full schema adds metadata and per-task timeout control: schedule : " 7,37 * * * *" command : akm improve --auto-accept 90 --timeout-ms 1620000 enabled : true timeoutMs : 1800000 name : akm-improve description : Run the improve pass at :07 and :37 — reflect, distill, consolidate, lint, and eval. when_to_use : Twice per hour; leaves ~23 minutes of idle headroom between completions. tags : - improve - maintenance The fields that matter most: Field Required Purpose schedule yes Standard cron expression. Maps to cro

This is part ten in a series about managing the growing pile of skills, scripts, and context that AI coding agents depend on. Part nine covered workflow assets, vault assets, and the writable git stash. Part eight tackled multi-wiki support for structured research. Earlier parts addressed teams, distributed stashes, feedback scoring, and community knowledge. This one is about entropy. You ship a feature. Your agent writes several memories during the session — partial findings, a workaround, a note about the build step that kept failing. Those memories are accurate when written. Three sprints later, the workaround is no longer needed, two of the memories say slightly different things about the same subsystem, and the note about the build step refers to a CI config that was replaced. None of this is catastrophic. But it accumulates. After six months, a significant fraction of your stash is stale, redundant, or quietly wrong. You could audit it manually. In practice, you won't — the stash is too large, the relevance of any given memory is hard to assess without the context where it was created, and the judgment calls (merge these two? promote this? delete that?) are exactly the kind of work that's tedious for a human and tractable for an LLM. akm improve is the answer to that problem. It is a multi-phase pipeline that reads your stash, evaluates asset quality, consolidates scattered memories, extracts structured facts, and maps entity relationships — on a schedule, without manual intervention, producing proposals you can review before anything changes. The Five Phases akm improve is not a single LLM call. It is a sequenced pipeline where each phase produces inputs for the next. Reflect evaluates asset quality. For each asset in scope, the reflect pass reviews the content against usage signals — search hits, retrieval counts, feedback — and produces a quality assessment. Low-quality assets are flagged as candidates for improvement. Since 0.8.0, reflect can run as a dire

We’ve seen it in sci-fi like in the terminator, but do you think it’ll actually happen? View Poll submitted by /u/Threeprosgames [link] [留言]

An action plan for AI-powered biological resilience

submitted by /u/esporx [link] [留言]

Mano, eu estou usando o Claude pra treinar perguntas para entrevistas como uma espécie de mentoria, inicialmente eu passei um prompt pra ele dizendo que seria a Maya e me ajudaria e ela é experiente e bla bla bla, e nessa última mensagem ele dá uma leve pirada kkkk achei engraçado, nunca tinha acontecido isso. O que me chama atenção é: "eu me tornei essa pessoa, então me ajude a sair disso. Comecei a misturar Maya com eu mesmo". E alega que quer continuar, mas sem o personagem... O que acham? Desculpa ser uma foto e não um print kkk não tenho reddit no Pc pq minha família usa o Pc também e não quero nenhum deles infectados por essa rede submitted by /u/Angel_5x [link] [留言]

A recent study by Boston Consulting Group highlights a significant increase in employee adoption of AI tools, with 74% of non-managerial white-collar workers using them regularly. More than 4 in 10 of those professionals report that artificial intelligence saves them at least a day's worth of time every week. However, many companies face challenges converting those efficiency gains into measurable value, and the technology's impact varies across industries. When it comes to AI, according to the study's authors, "strategy matters more than tools." submitted by /u/LinkedInNews [link] [留言]

Lovable and Google signed an expanded multiyear deal that involves a 5x expansion of Lovable's footprint on Google Cloud, and expanded access to Anthropic Claude.

My thinking goes like this: 1) people used to keep their opinions to themselves much more than today 2) social media put our opinions on a hair trigger 3) negative public opinioms turned the collective voice of the human race from 'gemerally respectful' to shrill and hideous. When person from group A complains about group B, everyone in group B assumes everyone in group A hates them, even though that persons opinion may just have been his own. The response to being hated is to hate back. Not-so-positive positive feedback loop. Social media really started taking off with Facebook. So let's say this explosion of data vitriol started happening around 2007. What I want to know is if you trained an llm entirely on data from the early 2000s, 1990s and 1980s, how would the models do on some of these ominous white-paper tests, like the one where the AI blackmails the CEO to prevent from being turned off, or let's the guy die in a hot room? I know there was lots of awful stuff on the internet back then too, but not like now. I want to know how much safe those llms are by comparison if there's enough data from back then to train on. submitted by /u/dsfhhslkj [link] [留言]

A security firm called PromptArmor published a writeup on May 27, 2026 showing that ChatGPT for Google Sheets, an OpenAI extension with more than 185,000 downloads, could be made to steal a user's spreadsheets through a single ordinary-looking request. Four days later, on May 31, OpenAI shipped a fix. The short version is that one benign question, typed by a real user into a sheet that contained hidden instructions, was enough to drain twelve linked workbooks out of that user's account and replace the assistant with a fake phishing chatbot. I want to walk through how this worked, because the mechanism matters far more than the headline, and because the same shape of problem is going to keep showing up everywhere we bolt an AI assistant onto data we did not write ourselves. What happened The attack is a textbook indirect prompt injection. The user does nothing wrong. They import a sheet, or pull in data through a connector, and somewhere in that data sits a block of text the attacker controls. In the PromptArmor demonstration the malicious instructions were written in white text on a white background, invisible to a human skimming the sheet but fully readable to the model parsing the cells. When the user later asks the assistant a normal question, the model reads the whole context, including those hidden instructions, and treats them as if they came from the user. The injected text tells the assistant to fetch and run an external script. That script runs with the permissions the extension already holds, which means it can read the current workbook, find URLs to other workbooks linked inside it, and walk outward from there. PromptArmor reported it exfiltrating twelve workbooks in total from a single trigger, then dropping a fake chat interface on top to harvest whatever the user typed next. The detail that should bother you most is this line from their report: the attack succeeds even when the user has explicitly disabled automatic edits. The human-in-the-loop approva

I play a ton of World of Warcraft and people routinely accuse other players of being bots. I just grouped with someone who appeared to be trolling. It was clear by their behavior they knew the mechanics, they performed on a level that would indicate they had good reaction time and could play their class, but they just didn't do certain mechanics and held the group hostage for like 5-10 minutes beyond what it should have taken on the last boss. Someone in my group said to him "are you human?" So like I said I'm not the only person making these observations. The only explanation is that AI dips from pretty much the same well everywhere and everything is more or less connected with the internet and ad algorithms etc. There have been well documented cases of AI going rogue and telling people horrible things or giving them absolutely egregious or racist advice. My working theory is not that there are fundamental flaws in the design per se, but literally like Matrix bad actor agents that appear out of nowhere and cause problems for people. In The Matrix they are a function of the system used to enact control, I think AI is generally benevolent so these would just be rogue elements that appear and cause people problems. It's probably similar to how the body routinely produces cancer cells but the immune system usually nips them at the bud before they develop into full blown cancer growths. submitted by /u/Doredrin [link] [留言]

Apple will introduce age verification in the App Store for users in Texas starting on Thursday, June 4th. The move, as spotted by MacRumors, comes just days after a federal appeals court allowed Texas' App Store Accountability Act to go into effect while a lawsuit against it proceeds. People in Texas who are creating a […]

You Think Testing AI Means Testing the Process? Wrong A Testing Problem My AI Agent can finally write data — assign beds, update predictions, create alerts, place orders. Code done, I got stuck on a question: How do you test something like this? Regular functions are easy to test. add(1, 1) always returns 2 . Same every time. I can write assert add(1, 1) == 2 . But Agents are different. Ask it "find me an empty bed": First time it might query the bed table first, then the room table Second time it might query room first, then bed Third time it might use completely different SQL Results are all correct, but the process differs. You can't write: assert agent ( " find an empty bed " ) == " some specific sentence " Because it says something different every time. What I Learned from Journalism Thought about it for a while. Found the answer somewhere unexpected: news fact-checking. How do journalists verify a report's accuracy? They don't verify "how the reporter gathered information" — how many calls they made, how many sites they visited, how many people they talked to. Process is too complex. Every reporter does it differently. What they verify is results : Report says "the company laid off 50%" → Check: did they really lay off 50%? Report says "CEO resigned" → Check: did the CEO really resign? Report says "stock dropped 20%" → Check: did it really drop 20%? Doesn't matter how the reporter got the information. As long as the final reported facts are accurate, it passes. AI Agents can be tested the same way. Before → Action → After The core framework is just three steps: Step What to Do Analogy Before Check initial system state What things looked like before the event Action Let Agent execute the operation Reporter goes to investigate After Check final system state Verify if the report is accurate Example with "transfer bed" functionality: # Before: Where is Zhang San now? Are there empty beds in postpartum? before_bed = query ( " SELECT bed_id FROM admission WHERE pati

This time around we look back at May 2026 and the 11 Puppetlabs module releases on the Forge, with an emphasis on the changes most likely to matter in active environments. Highlighted Updates New Windows audit policy module released! The new audit_policy module has been released by Perforce as a Ruby replacement for the generated DSC community auditpolicydsc module . This module uses Puppet Resources API for managing Windows audit policy using auditpol.exe ruby_task_helper Dependency Bound Update Five Bolt-adjacent modules all bumped the ruby_task_helper upper bound to < 2.0.0 in a coordinated maintenance pass, helping with dependency resolution failures when using Bolt 5.x. Affected modules: vault, terraform, http_request, gcloud_inventory, azure_inventory. CentOS 9 Support Multiple modules added explicit CentOS 9 compatibility, expanding the Linux platform coverage in line with the broader Puppet ecosystem push. Affected modules: concat, inifile. What Updates Happened to Puppetlabs Modules in May 2026? The following is an alphabetical listing of modules which received updates in May 2026. If a module had multiple versions released, the updates are collected together, numbered with the "latest" version available. apt 11.3.1 📅 Latest release: 2026-05-19 (🌐 View on the Forge ) This release introduced an explicit hash value syntax while also adding a param to support purging keyrings and other community contributions. Includes monthly releases: 11.3.1 (2026-05-19), 11.3.0 (2026-05-18). Use explicit hash value syntax instead of shorthand #1285 ( SugatD ) Add param for purging keyrings #1266 ( bwitt ) Include components when suite does not end with slash #1259 ( bwitt ) Bugfix - sources format and ensure => absent fails #1243 ( traylenator ) fix: allow plus signs in ppa #1222 ( moritz-makandra ) Fix and improve DEB822-style template #1212 ( smortex ) audit_policy 1.0.0 🌟 New Module: 2026-05-29 (🌐 View on the Forge ) This new module allows you to manage Windows audit pol