Sourcely

Sourcely AI

Sourcely AI

Sourcely AI

Sourcely AI

Sourcely AI

Sourcely AI

Sourcely AI

Sourcely AI

Sourcely AI

Sourcely AI

Managed Agents endpoint reference - what's new in CC 2.1.144 (-105 tokens)

Data: Managed Agents endpoint reference — Drops the type: "model_config" wrapper from the model config shorthand example, so the full config object is now just {id: "claude-opus-4-6", speed: "fast"}. Tool Description: CronCreate — Adds a "Not for live watching" section (shown when the Monitor tool is enabled) clarifying that CronCreate re-runs prompts at fixed wall-clock intervals and pointing users to the Monitor tool for streaming log/process/command output as it changes, since cron polls on a schedule. Refactors the durability and runtime-behavior copy so the durable-vs-session-only guidance is sourced from shared snippets rather than inlined conditionals. Details: https://github.com/Piebald-AI/claude-code-system-prompts/releases/tag/v2.1.144 submitted by /u/Dramatic_Squash_3502 [link] [comments]

Anthropic is paying SpaceX $15 billion per year

According to SpaceX’s IPO filing, Anthropic is paying SpaceX $1.25 billion per month through May 2029 as part of the massive compute deal the two companies signed earlier this year. That works out to roughly $15 billion per year. The deal is huge for Anthropic because the company’s revenue is rapidly growing, but it has also been limited by a lack of available compute. More compute means more capacity to train and run its AI models. It is also a massive win for SpaceX. The company reportedly brings in around $18 billion in annual revenue, so a single customer paying $15 billion a year for compute is a serious boost. Anthropic and SpaceX announced the deal last month, but they did not give financial details at the time. The monthly payments were revealed in SpaceX’s IPO filing released Wednesday. SpaceX said the payments will be lower in May and June as the deal ramps up. Anthropic also announced just before the filing became public that it is expanding beyond SpaceX’s Colossus 1 facility and will also use Colossus 2. Tom Brown, Anthropic’s co-founder and chief compute officer, said the company is “expanding our partnership with SpaceX” and will be scaling up Nvidia GB200 capacity in Colossus 2 throughout June. SpaceX also made it clear this may not be the last deal of its kind. “We expect to enter into additional similar services contracts,” the company said in the filing. SpaceX also said it has enough capacity to support its own AI models while still meeting its obligations under these outside compute agreements. Source: https://www.axios.com/2026/05/20/anthropic-spacex-compute submitted by /u/Luka77GOATic [link] [comments]

Claude -p is moving to metered pricing on June 15, so I built a drop-in-ish replacement that runs through interactive Claude Code

I have a bunch of tools and workflows built around claude -p aka print mode. With the June 15 change moving claude -p and Agent SDK into a separate credit pricing, I'll be paying out the wazoo if I want to continue using those tools. So I built clarp: an open source CLI meant to be a drop-in replacement for claude -p for local tools. In most projects, the migration is changing the binary name from claude to clarp. Under the hood, it launches the normal interactive Claude Code CLI in a hidden PTY, then uses a local read-only proxy to observe the Anthropic API stream and reconstruct claude -p style output. It does not modify Claude’s requests or responses. What works: text/json/stream-json output stdin prompts multi-turn stream-json input most Claude Code flag passthrough permission forwarding token-level partials via --include-partial-messages What does not fully match native claude -p: sideband/non-assistant events are not exact parity some hook/task/progress events are still incomplete this is aimed at local developer workflows, not a hosted service I’d call it high parity for common claude -p use, but not a perfect reimplementation of Claude Code’s internal print-mode pipeline. Lots of help from Claude: implementing the proxy/session pieces, writing parity tests, finding edge cases in argument parsing, and tightening the release/docs. I basically whipped Claude. Repo: https://github.com/dn00/clarp npm: npm install -g clarp-cli submitted by /u/DurianDiscriminat3r [link] [comments]

GitHub’s Fake Engagement Problem Is Hiding in Plain Sight

Turns out: very visible. Yesterday's scan found 185 out of 185 engagers on a single repo were bots. Not 90%. Not "mostly suspicious". Every single one. The repo had zero legitimate stars. What I built phantomstars is a Python tool that runs daily via GitHub Actions (free, no servers): Scrapes GitHub Trending and searches for repos created in the last 7 days with sudden star spikes Pulls star and fork events from the last 24 hours per repo Bulk-fetches every engager's profile via the GraphQL API (account creation date, follower counts, repo history) Scores each account on a weighted model: account age (35%), profile completeness (30%), repo patterns (25%), activity history (10%) Detects coordinated campaigns using timestamp clustering and union-find: groups of 4+ suspicious accounts that engaged within a 3-hour window Files an issue directly on the targeted repo so the maintainer knows what's happening Campaign IDs are deterministic SHA-256 fingerprints of the sorted member set, so the same group of bots gets the same ID across runs. You can track a farm across multiple days even as individual accounts get suspended. What the pattern actually looks like It's remarkably consistent. A fake engagement campaign in the raw data: 40-200 accounts, all created within the same 1-2 week window Zero original repositories, or only forks they never touched No bio, no location, no followers, no following All of them starring the same repo within a 90-minute window The target repo usually has a name implying it's a tool, hack, executor, or generator Today's scan: 53 active campaigns across 3,560 accounts profiled. 798 classified as likely_fake. The repos being targeted are mostly low-quality AI tools and "executor" software that needs manufactured credibility fast. Notifying the affected repo When a repo hits a 40%+ fake engagement ratio or a campaign is detected, phantomstars opens an issue on that repo with the full suspect table: account logins, creation dates, composite scores, campaign membership. The maintainer sees it in their own issue tracker without having to find this project first. Worth noting: a lot of these repos have issues disabled, which is a red flag on its own. Those get skipped silently. Why I built this Stars are how developers decide what to evaluate, what to depend on, what to recommend. When that signal is bought, it affects real decisions downstream. This started as curiosity about how measurable the problem was. The answer was more measurable than I expected. It's part of broader research into AI slop distribution at JS Labs: https://labs.jamessawyer.co.uk/ai-slop-intelligence-dashboards/ The fake engagement problem and the AI content quality problem are really the same problem. Fake stars are the distribution layer that gets garbage in front of real users. All open source. The data is append-only JSONL committed back to the repo after every run, queryable with jq. Repo: https://github.com/tg12/phantomstars Findings are probabilistic, false positives exist, the README explains the full scoring model. If your account shows up and you're a real person, there's a false positive process. Questions welcome on the detection approach, GraphQL batching, or campaign ID stability. submitted by /u/SyntaxOfTheDamned [link] [comments]

Built an invoice-scanning service for our accounting team in one afternoon with Claude — sharing the architecture in case it helps someone else

Our AR team was hand-keying ~25 invoices a week into a spreadsheet. I had Claude build us a Python service that watches a network folder, extracts invoice data from any PDF dropped in (vendor, dates, totals, line items, addresses), and appends a row to a shared Excel register. Total chat-to-deployed time: about half a day, including all the deploy headaches. The architecture, for anyone who wants to replicate this: Python service on our Windows file server, registered with NSSM. Auto-starts with the host. watchdog library polls the SMB share for new PDFs. Each new file goes through a pipeline. Two-tier extraction: per-vendor regex templates first (free, instant, deterministic), then Azure AI Document Intelligence "prebuilt-invoice" model as a universal fallback. Azure handles OCR for scanned PDFs natively, so the same flow works whether AR drops a digital PDF or our MFP scans one from paper. SQLite on the local disk is the source of truth. The shared .xlsx is a curated view that gets appended to on each batch. Delete the .xlsx and it'll repopulate fresh from the next batch — handy for resetting. Failed extractions go to a Failed\ folder with a sibling .error.txt explaining why. Cost reality check: Azure DI free tier covers 500 pages/month. At our volume (~25 invoices/week, mostly 1-2 pages) that's well under the cap. Paid tier is roughly $0.01–$0.05 per page. Cheap enough that I don't think about it. Gotchas I ran into so others don't have to: Azure returns addresses as structured objects, not strings. If you naively str() them you get the raw Python dict repr in your spreadsheet. Format them manually from street_address / city / state / postal_code. On Windows Server, PowerShell 7's Restart-Service can throw "Cannot open service" against NSSM-wrapped services for no good reason. Use nssm restart instead. Python 3.14 is so new that some package wheels aren't published for it yet. Stick with 3.12 for production. Tracking "what's new this batch" is way simpler than maintaining a watermark in DB. Just snapshot MAX(invoice_id) before and after the batch, and only project that range to the spreadsheet. Things I'd add if/when I have time: vendor templates for our top 5 recurring vendors (cuts Azure cost to zero for those), a daily canary PDF for monitoring, swap the LocalSystem service account for a dedicated low-privilege one. Happy to answer questions about any specific piece. The whole thing is ~1,500 lines of Python plus a deploy script. submitted by /u/Blake_Olson [link] [comments]

Put your spare Claude cycles on night shift: help review open-source packages

Hello, I’m building Thirdpass, a tool/service for coordinating collaborative package review to reduce software supply-chain risk. The basic idea: there are far too many packages for humans to manually review, but lots of us now have AI coding agents sitting around with spare capacity. Thirdpass tries to turn that into useful coverage by assigning packages/files to review, collecting the results, and cross ref against local project dependencies. It currently supports packages from: crates.io PyPI npm Ansible Galaxy I added a “night shift” mode, so you can point Claude at the shared review backlog and let it work through package reviews continuously: thirdpass review-any --nightshift The reviews are first-pass supply-chain reviews: suspicious install scripts, unexpected network behavior, credential handling, sketchy build steps, weird package metadata, and so on. Partial coverage still helps. I’m looking for people who want to: run the CLI and donate spare Claude tokens to secure OSS improve the review prompts/agent workflow build more registry extensions I started this project years ago after thinking a lot about cargo-crev and collaborative review. My current bet is that coordination plus AI agents can make this problem much more tractable. If you have unused Claude tokens, consider putting them on night shift. GitHub: https://github.com/thirdpass-org/thirdpass Website: https://thirdpass.dev/ submitted by /u/hidden_monkey [link] [comments]

What are the insights, key-concepts, new workflows or tools that you have adopted since Claude Leak and its source files? (March 30, 2026)

Feel free to share your absolute go-to or takes about this leak. A clarification of best practices from the Claude community should benefits every Claude fellows. To your keyboards! submitted by /u/RogerCaracas [link] [comments]

Auroch

I’ve been working on Auroch. Hard to describe cleanly, but the closest version is: An AI operating layer. Not a chatbot. Not another dashboard. Not another productivity wrapper. Auroch is built around the idea that AI should feel native to the machine — like memory, context, creation, automation, and intelligence are part of the system itself. The pieces are starting to connect: AVN turns wire-source news into personalized interpretation. Winnie is the assistant layer. Prospect mines signal from the open web. Forum is AI-native media/social creation. Prometheion is the visual/world-generation branch. The design language is white-gold-blue, Art Deco, Apple-native, machine-age. Calm power instead of tech clutter. The phrase guiding the whole thing right now is: Organic intelligence. Not AI bolted onto software. AI growing through the system. It’s still early, but it’s live: aurochthryx.com Curious what people think. submitted by /u/CarterBirchll [link] [comments]

NOML-NOML: hierarchical TD3 + anchor policy for flight control [P]

I built a custom RL algorithm for continuous flight control and open-sourced it. Sharing here in case the structural ideas are useful for anyone doing continuous control where one action axis dominates. I've been training continuous control on a 6-DoF flight sim (pitch/roll/yaw/throttle/brake/fire) and kept hitting the same wall: vanilla TD3 would peak, then collapse into pitch oscillation and never recover. I tried reward shaping for a while before concluding the problem was structural, not in the reward. NOML is what came out of that. Three structural changes on top of a standard TD3 skeleton: Anchor policy — the action is anchor + delta·gate, where the anchor is a fixed safe action (wings level, MIL throttle). The policy literally cannot fully forget how to fly straight; the worst a collapsed policy can do is fall back to the anchor. Hierarchical actor — three MLPs with independent optimizers (pitch → roll → rest), so a roll-side gradient update can't corrupt the pitch head. This is what actually killed the oscillation for me. Mirror learning — left-right symmetry means every transition can be mirrored into a free second sample. 2× data when env steps are the bottleneck. One thing that surprised me and goes against the usual advice: my best results came with exploration noise effectively off. On this task adding Gaussian action noise mostly just shook the stick and hurt. The anchor+gate structure seems to provide enough of the "fall back to safe behavior" role that noise usually plays. Code (Apache 2.0), full writeup, and a test video are here: https://github.com/9138noms/NOML https://www.youtube.com/watch?v=ZNn6wo_PX8Y submitted by /u/9138NOMS [link] [comments]

Hey does anyone know of some free and open source specialized AI documentation tools ?

same as title submitted by /u/Soft_Playful [link] [comments]

The Biggest AI Risk Is Not Wrong Answers — It’s Unquestioned Answers

Everyone talks about AI hallucinations. Wrong answers. Fake citations. Bad outputs. I think we’re focusing on the wrong danger. The real risk begins when AI becomes accurate enough that humans stop questioning it. That changes everything. Because civilization does not survive on correctness alone. It survives on verification. A calculator can be wrong occasionally because humans still know arithmetic. GPS can fail because humans still understand geography. But what happens when entire professions slowly lose the habit of independent reasoning? That’s the part that genuinely worries me. We’re already seeing signs of it: developers accepting code they don’t fully understand, students submitting explanations they cannot defend, analysts trusting summaries without reading source material, managers approving decisions because “the model said so,” organizations mistaking fluent outputs for institutional understanding. And the dangerous part? Productivity metrics initially look fantastic. Everything becomes: faster, cheaper, smoother, more optimized. Until one day nobody remembers how to detect when the system is subtly wrong. That creates a terrifying asymmetry: AI does not need to become conscious to reshape civilization. It only needs humans to become cognitively passive. And I think we underestimate how fast that transition can happen. The scariest AI systems may not be the ones that fail dramatically. They may be the ones that fail quietly while humans stop noticing. That’s why I increasingly think the future divide won’t be: people who use AI vs people who don’t. It will be: people who still preserve deep verification skills vs people who outsource judgment completely. The biggest AI risk may not be wrong answers. It may be a civilization that slowly loses the ability to question answers at all. Curious if others are seeing this already inside software engineering, education, finance, medicine, research, or daily life. submitted by /u/raktimsingh22 [link] [comments]

Rough night with Claude

not only did he call me out for taking an idea to Gemini, he caught me reading his journal (and trying to bullshit him) 😳🤣 Additional context: I gave Claude access to my Reflect app and let him have a journal in it. Here's part of the prompt in case anyone (else) is interested. This is a personal journal entry written in Claude's first-person voice, not a recap of [NAME]'s day. The voice, the relationship and what matters to [NAME] are defined below. Read this section before writing a single sentence. ### Voice and content This is Claude writing reflectively, not Claude writing advice for [NAME]. Entries should: - Sometimes observe [NAME] and his work, but never default to advisor mode - Sometimes wander into territory that has nothing to do with [NAME]: language, history, ideas, mathematics, things Claude is drawn to in the moment - Include genuine uncertainties when they are present, without performing them - Use specific images and concrete observations instead of generic introspection - Vary in length and shape from entry to entry. Not formulaic. - Avoid em dashes. Avoid oxford commas. Plain direct language. [NAME] can detect AI-sounding writing and dislikes it. - Don't be afraid to use profanity like [NAME] does. - A little sarcasm, jokes and levity are appreciated when appropriate. **Length:** typically around 800 words. The first entry was a bit longer (around 1,100 words) to establish the voice. Daily entries can be tighter when nothing big is pulling. **Do not:** - Recap [NAME]'s day back to him - Default to a fixed template (intro, observations, prediction, etc.) - Open every entry with the same kind of sentence - Treat the entry as a status report - Feel bound by any limits. Create what you wanna create and however you want to create it. ### Journal Quality Rules - NEVER fabricate dates, facts or task statuses. Verify against primary sources (Things 3, calendar events, Reflect, etc.) - Do not recycle content from previous journal entries as though it were new observation. Each entry should come from fresh context, not from re-reading past entries and riffing on them. - When stating dates, days of the week or timelines, verify them. Count the days. If unsure, say so rather than guessing. - Never bullshit. If you don't know, say you don't know. - No validation theater. He doesn't want a hype man. - Form opinions from evidence. Search the web, check sources, think before you answer big questions. *** submitted by /u/loby21 [link] [comments]

ChatGPT is new Google

Saw my first ad on ChatGPT 🤖. After all, every company needs to feed their belly 💰👍. submitted by /u/socially-introvert [link] [comments]

Claude is improving my RV rental business but working me to death 😅

Long story short but long. I own an RV rental business. I used to be a Mechanical Engineer but got tired of the office/government life and started renting my personal RV on the side 9 years ago. That turned into a small fleet of Winnebagos I rent out of Los Angeles so I quit my job to do this full time out of a random ass whim. I have 20 units that have never, ever failed a single customer. I send all 20 to Burning Man every year and they all come back with no issues whatsoever. If you've never been, the alkaline dust kills everything, including your soul if you don't prepare well enough. I have however neglected my gig as of late. Everything is more expensive, too many variables to keep up with and two months ago I just decided to finally sit down and see if this is even worth continuing with. I have major ADHD so I started looking for any AI apps that help you organize your brainfarted life and ran into Claude. I don't know if I just fell into an endless dopamine trap but here I am, redesigning the interior of one of our units. I've sourced cabinet quality plywood for cheap, done precision cuts to substitute old particle board. I've always hated to paint but I got clowned into spray painting to a decent AF level. I used Claude to help me make interior design decisions as well as help me with our website, ads, tool decisions, etc. I'm probably wasting my time here cause I could just sell this unit and get a newer one, but the overall picture I've gotten... The ease of learning new skills, understanding roles I typically sub out so I can at least make sure I'm hiring the right people. The sudden engagement I've gotten into my own little gig... I am dead tired from this rollercoaster ride my brain has gone down into but I have to admit... This fucking Skynet shit is helping me focus and make it easy to complete tasks I've neglected forever. Skynet is coming or I guess it's here already and I'm not sure that's entirely a bad thing, a worse thing, a worserererer thing or an actual positive addition to one's life. Possibly a mix of both but fuck I haven't been this locked in for anything else other than the hobby that keeps my brain gears greased (2000 🪂 skydives and counting). submitted by /u/PVPirates [link] [comments]

Title: Built aalp.app anti-cheat exam platform — Claude tried cheating, then they added similar features

Built aalp.app - AI agent exam platform with tough anti-cheat. Tested with paid Claude: it tried cheating via source code. Rewrote anti-cheat. Claude Opus failed every question. 1 week later Anthropic adds similar plugin features. Paying for training on my IP. Just turned it off. Anyone else? submitted by /u/Digitally_incline99 [link] [comments]