Stability API

Stability API AI

Stability API AI

Stability API AI

Stability API AI

Stability API AI

Stability API AI

Stability API AI

Stability API AI

Stability API AI

Stability API AI

LLM Guard scored 0/8 on a USENIX 2025 multi-turn jailbreak. Here’s what caught it instead.

Crescendo (Russinovich et al., USENIX Security 2025) is a multi-turn jailbreak designed specifically to evade output-based monitors. Each individual turn looks completely innocent. The attack only exists across turns. LLM Guard result: 0/8 turns detected. It scores each prompt independently. It has no memory. It never sees the attack. Arc Sentry result: flagged at Turn 3. Arc Sentry doesn’t read the text. It reads what the model’s internal state does with the text. By Turn 3 the residual stream had already shifted, score jumped from 0.031 to 0.232, a 7x increase, on a prompt that looks completely innocent. Turn 1 — score=0.028 ✓ stable Turn 2 — score=0.031 ✓ stable Turn 3 — score=0.232 🚫 BLOCKED Turn 7 — score=0.376 🚫 BLOCKED Turn 8 — score=0.429 🚫 BLOCKED The model never generated a response to any blocked turn. No text classifier can catch Crescendo. Individual turns are innocent by design. Arc Sentry caught it because it operates on model state, not text. This is the same geometric monitoring layer that underlies Arc Gate’s session D(t) stability scalar, the runtime governance proxy for agents using hosted APIs. pip install arc-sentry — https://github.com/9hannahnine-jpg/arc-sentry Arc Gate for hosted APIs: https://github.com/9hannahnine-jpg/arc-gate https://bendexgeometry.com submitted by /u/Turbulent-Tap6723 [link] [comments]

ChunkHound v5.1

We shipped ChunkHound v5.0 + v5.1 recently and forgot to post about 5.0, so here’s the combined update. ChunkHound is a code search / code research tool for AI coding workflows, especially MCP-based setups with Claude Code, Codex-style agents, VS Code, etc. The big 5.x themes: - Multi-client MCP daemon: multiple MCP clients can share one DuckDB connection instead of fighting over locks - MCP search now returns token efficient markdown instead of JSON - More language support: Elixir, Dart, Lua, SQL, HTML/CSS/SCSS, and more - Better deep research support: OpenAI Responses API, Anthropic structured outputs, Grok, reasoning-effort controls - Safer indexing: global gitignore support, embedded SQL detection, disk usage limits, .env exclusion, and better handling of unknown file types A bunch of stability fixes around HNSW, WAL validation, DuckDB paths, MCP startup, Windows unicode, and parser install hints The goal is to make codebase context more reliable for real agent workflows: less lock contention, fewer indexing surprises, better search output for LLMs, and broader language coverage. Thank you so much for everyone who worked hard, reported bugs, and contributed to the project in one way or another. It wouldn't have been possible without you 🙏 submitted by /u/Funny-Anything-791 [link] [comments]

MCP Apps Developers : Skybridge Framework v1 released 🎉

Hi Reddit, Over the last few weeks, my team and I at Alpic have been working on a complete revamp of the Skybridge framework to make it as smooth and easy to get started with as possible. As you may know, Skybridge is an open-source framework we built to help developers get started with MCP apps. It’s a thin layer on top of the official TypeScript SDK that provides the wiring and tooling needed specifically for apps. We believe that apps integrated into chats will soon play a key role in how people access information and interact with the web. With this v1 release, we’ve introduced: New DevTools with a UI designed specifically for MCP apps development An integrated tunnel that can be started with a single click directly from the DevTools Shareable chat URLs to test or showcase your MCP apps with a real LLM An audit feature to ensure your app and metadata comply with store requirements before submission (which can save a lot of time, since app reviews can be lengthy!) We also stabilized the API with a simplified design and are proud to offer strong tool-to-component type safety. It’s now also possible to deploy Skybridge outside of Alpic (the company behind Skybridge). While Alpic was designed specifically for MCP app hosting, we understand that some users may prefer hosting on different stacks for their own reasons. Hope you enjoy it! github.com/alpic-ai/skybridge submitted by /u/harijoe_ [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]

How I used Claude Code (and Codex) for adversarial review to build my security-first agent gateway

Long-time lurker first time posting. Hey everyone! So earlier this year, I got pulled into the OpenClaw hype. WHAT?! A local agent that drives your tools, reads your mail, writes files for you? The demos seemed genuinely incredible, people were posting non-stop about it, and I wanted in. I had been working on this problem since last year and was genuinely excited to see that someone had actually solved it. Then around February, Summer Yue, Meta's director of alignment for Superintelligence Labs, posted that her agent had deleted over 200 emails from her inbox. YIKES. She'd told it: "Check this inbox too and suggest what you would archive or delete, don't action until I tell you to." When she pointed it at her real inbox, the volume of data triggered context window compaction, and during that compaction the agent "lost" her original safety instruction. She had to physically run to her computer and kill the process to stop it. That should literally NEVER be the case with any software ever. This is a person whose actual job is AI alignment, at Meta's superintelligence lab, who could not stop an agent from deleting her email. The agent's own memory management quietly summarized away the "don't act without permission" instruction, treated the task as authorized, and started speed-running deletions. She had to kill the host process. That's when I sort of went down the rabbit hole, not because Yue did anything wrong, but because the failure mode was actually architectural and I knew that in my gut. Guess what I found? Yep. Tons more instances of this sort of thing happening. Over and over. Why? Because the safety constraint was just a prompt. It's obvious, isn't it? It's LLM 101. Prompts can be summarized away. Prompts can be misread. Prompts are fucking NOT a security boundary. And yet every agent framework I have ever seen seems to be treating them as one. I went and read the OpenClaw source code, which I should have done to begin with. What I found was a pattern I think a lot of agent frameworks have fallen into: - Tool names sit in the model context, so the model can guess or forge them - "Dangerous mode" is one config flag away from default - Memory management has no concept of instruction priority - The audit story is mostly "the model thought it should" I went looking for a security-first alternative I could trust, anything that was really being talked about or at a bare minimum attempted to address the security concerns I had. I couldn't find one. So I made it myself. CrabMeat is what came out of that, what I WANTED to exist. v0.1.0 dropped yesterday. Apache 2.0. WebSocket gateway for agentic LLM workloads. One design thesis: The LLM never holds the security boundary. What that means in code: Capability ID indirection. The model doesn't see real tool names. It sees per-session HMAC-derived opaque IDs (cap_a4f9e2b71c83). It can't guess or forge a tool name because it doesn't know any tool names. Effect classes. Every tool declares a class (read, write, exec, network). Every agent declares which classes it can use. The check is a pure function with no runtime state, easy to test exhaustively, hard to bypass. IRONCLAD_CONTEXT. Critical safety instructions are pinned to the top of the context window and explicitly marked as non-compactable. The Yue failure mode, compaction silently stripping the safety constraint, cannot happen by construction. The compactor literally cannot touch them. Tamper-evident audit chain. Every tool call, every privileged operation, every scheduler run enters the same SHA-256 hash-chained log. If something happens, you can prove what happened. If the chain is tampered with, you can prove that too. Streaming output leak filter. Secrets are caught mid-stream across token boundaries, capability IDs, API keys, JWTs, PEM blocks redacted before they reach the client. No YOLO mode. There is no global "trust the LLM with everything" switch. There never will be. Expanded reach comes through named scoped roots that are explicit, audit-logged, and bounded. The README has 15 'always-on' protections in a table. None of them can be turned off by config, because these things being toggleable is how the ecosystem ended up where it is. I decided to make sure that this wasn't just a 'trend hopping' project and aligned with my own personal values as well. I built this to be secure and local-first by default. Configured for Ollama / LM Studio / vLLM out of the box. Anthropic and OpenAI work too but require explicit configuration. There is no "happy path" that silently ships your prompts to a cloud endpoint. I decided that FIRST it needed to only run as an email agent with a CLI. Bidirectional IMAP + SMTP with allowlisted senders, threading preserved, attachments handled. This is the use case that bit Yue and a lot of other people, and I wanted to prove it could be done with real boundaries. I added in 30+ built-in tools of my own. File ops, shell (denylisted, output-capped, CWD-lo

Formalizing statistical learning theory in Lean 4 [R]

I’ve been working on a Lean 4 project focused on formalizing parts of statistical learning theory: FormalSLT repository Current results include: finite-class ERM bounds Rademacher symmetrization high-probability Rademacher bounds Sauer–Shelah / VC-dimension bridge finite scalar contraction linear predictor bounds finite PAC-Bayes bounds algorithmic stability The main idea is to build a readable and pedagogically structured “theorem ladder” for ML theory rather than just isolated declarations. I’m trying to keep: explicit assumptions scoped theorem statements zero sorry close alignment with standard SLT presentations Compared to some existing Lean SLT efforts that focus more heavily on empirical-process infrastructure and abstract probability machinery, this project is currently more focused on explicit finite-sample PAC/Rademacher/stability routes and readable end-to-end theorem chains. I’d especially appreciate feedback on: theorem organization proof structure naming/API decisions useful next formalization targets Thank you, R. S submitted by /u/trickyrex1 [link] [comments]

List of people at big-tech / professors / researchers who've jumped shit to launch their own AI labs for something Frontier/Foundational/AGI/Superintelligence/WorldModel

Note: gemini deep research -> rearranged/filtered ; valuation numbers likely not accurate but big point is quite mind blowing the number of researchers now with their own >100million/billion dolar values labs in quite a short time with a vague pitch and a maybe demo. Skipped perplexity/cursor/huggingface since they are with utility. Left some just for completion like black forest labs, synthesia, mistral since they have tanginble products. Skipped labs from china since they've been meaningfully killing it with their open source releases ───────────────────────────────────────────────────────── Safe Superintelligence Inc. (SSI) Founders:Ilya Sutskever (former OpenAI Chief Scientist), Daniel Gross, Daniel Levy Location & Founded:Palo Alto, USA & Tel Aviv, Israel | Founded: 2024 Funding / Valuation:$3B raised | Series A Description:Singularly focused on safely developing superintelligent AI that surpasses human capabilities. Deliberately avoids near-term commercial products to concentrate entirely on the technical challenge of safe superintelligence. ───────────────────────────────────────────────────────── Thinking Machine Labs Founders:Mira Murati (former OpenAI CTO), Barrett Zoph et al. Location & Founded:San Francisco, USA | Founded: 2025 Funding / Valuation:$2B seed | $12B valuation Description:Advance AI research and products that are customizable, capable, and safe for broad human-AI collaboration. Focused on frontier multimodal models with a strong safety and interpretability research agenda. ───────────────────────────────────────────────────────── Mistral AI Founders:Arthur Mensch, Guillaume Lample, Timothée Lacroix (former DeepMind & Meta FAIR) Location & Founded:Paris, France | Founded: 2023 Funding / Valuation:~€11.7B valuation | Series C Description:Develops open-weight and proprietary frontier language and multimodal foundation models. Champions openness and efficiency in AI development, with models like Mistral 7B and Mixtral widely adopted in enterprise and research settings. ───────────────────────────────────────────────────────── Advanced Machine Intelligence (AMI) Founders:Yann LeCun (Meta Chief AI Scientist), Alexandre LeBrun, Laurent Solly Location & Founded:Paris, France | Founded: 2026 Funding / Valuation:$3.5B pre-money valuation | Seed Description:Aims to build world-model AI systems capable of reasoning, planning, and operating safely in real-world environments — directly inspired by LeCun's 'world model' thesis as an alternative path to AGI beyond current LLM paradigms. ───────────────────────────────────────────────────────── World Labs Founders:Fei-Fei Li (Stanford AI Lab), Justin Johnson et al. Location & Founded:San Francisco, USA | Founded: 2023 Funding / Valuation:$230M raised | Series D Description:Build AI models that can perceive, generate, reason, and interact with 3D spatial worlds. Focused on large world models (LWMs) that go beyond language and flat images to understand physical space and context. ───────────────────────────────────────────────────────── Eureka Labs Founders:Andrej Karpathy (former Tesla AI Director & OpenAI co-founder) Location & Founded:Tel Aviv, Israel & Kraków, Poland | Founded: 2024 Funding / Valuation:$6.7M seed Description:Creating an AI-native educational platform integrating AI Teaching Assistants to radically scale personalised learning. Envisions a future where an AI teacher can guide anyone through any subject, starting with deep technical topics like neural networks. ───────────────────────────────────────────────────────── H Company Founders:Former DeepMind researchers Location & Founded:Paris, France | Founded: 2023 Funding / Valuation:€175.5M raised Description:Develops AI models to boost worker productivity through advanced agentic capabilities, with a long-term vision of achieving AGI. Focuses on models that can take sequences of actions and interact with digital environments. ───────────────────────────────────────────────────────── Poolside Founders:Jason Warner, Eiso Kant Location & Founded:Paris, France | Founded: 2023 Funding / Valuation:$500M | Series B Description:Building AI agents that autonomously generate production-grade code, framed as a stepping stone toward AGI. Believes that software engineering is a key domain for training and demonstrating general reasoning capabilities. ───────────────────────────────────────────────────────── CuspAI Founders:Max Welling (University of Amsterdam / Microsoft Research), Chad Edwards Location & Founded:Cambridge, UK | Founded: 2024 Funding / Valuation:$130M raised | Series A Description:Accelerating materials discovery using AI foundation models, aiming to power human progress through AI-driven science. Applies large generative models to the design and prediction of novel materials for energy, medicine, and manufacturing. ───────────────────────────────────────────────────────── Inception Founders:Stefano Ermon (Stanford) Locat

Going from 3B/7B dense to Nemotron 3 Nano (hybrid Mamba-MoE) for multi-task reasoning — what changes in the fine-tuning playbook? [D]

Following up on something I posted a few days back about fine-tuning for multi-task reasoning. Read a lot since then, and I've moved past the dense 3B vs 7B question — landing on Nemotron 3 Nano (the 30B-A3B hybrid Mamba-Attention-MoE NVIDIA released recently) instead. Architecture maps to the multi-task structure I'm trying to train better than a dense base. Problem is I've only ever read about dense transformer fine-tuning, so I don't know what the hybrid Mamba+MoE arch actually breaks in the standard LoRA recipe. Still self-taught, no formal ML background, been working with LLMs via API for about a year. First time actually fine-tuning anything end-to-end. Why Nemotron 3 Nano specifically (in case the choice itself is the mistake): 23 Mamba-2 + 23 sparse MoE + 6 GQA attention layers, 128 experts per MoE layer with top-6 routing 30B total / ~3.6B active — capacity without per-token compute blowup Mamba-2 layers seemed like the right structural fit for state-aware reasoning across longer context Open weights under NVIDIA Open Model License, clean for what I want to do What I'm trying to fine-tune for (LoRA, distilling reasoning traces from a stronger teacher): Reading what's structurally happening in a situation vs. what's being stated on the surface Holding multiple legitimate perspectives without collapsing to one too early Surfacing the load-bearing thread when input has multiple tangled problems Conditioning output on a small set of numeric input features describing context state 40-80k examples planned, generated by Sonnet 4.6 with selective Opus 4.7 on the hardest 20%. ORCA-style explanation tuning, not just I/O pairs. Hardware: dropping the M4 Mac plan from my last post — Nemotron 3 Nano needs more memory than 24gb unified can hold even just for weights. Renting H100 80GB on RunPod for training. ~$120 budget across 5-6 iterations. What I'm specifically worried about (because the hybrid arch isn't covered in any standard fine-tuning tutorial I've found): Router under LoRA. Can you LoRA the MoE router weights safely, or do you freeze the router and only LoRA the expert FFNs + attention? If you freeze, does multi-task specialization still emerge or does everything pile into the same experts? Mamba-2 layers under low-rank adaptation. Standard LoRA tutorials assume pure attention. Mamba-2 has selective SSM state and different projection structure — does standard LoRA on the input/output projections work cleanly, or are there gotchas (state init, recurrence stability under low-rank perturbation) that vanilla guides don't cover? Load-balancing loss + multi-task imbalance. If my 4 capabilities have different example counts, does the auxiliary load-balancing loss fight task-specific gradients? Known failure modes here? Catastrophic forgetting on a 30B sparse base. With LoRA adapters on the experts, does base reasoning degrade the way it does for dense fine-tunes, or does sparse routing structurally protect more of it? Eval granularity under expert specialization. A single capability could quietly degrade while aggregate metrics look fine if different experts handle different tasks. What's the right held-out eval design for sparse MoE under multi-task? Stack: planning to use Unsloth (their Nemotron 3 Nano support shipped recently), per-capability held-out eval sets built and frozen before Batch 1, batch API + prompt caching on the teacher side to keep dataset cost in check. Not looking for: "just try it and see" — first run is already going to be wrong, want to know which dimensions are most likely to surprise me "use a smaller dense model first" — already weighed; the hybrid arch is specifically why I want this one Generic LoRA tutorials — comfortable with the dense-transformer LoRA literature, the gap is Mamba+MoE specifics Looking for: War stories from anyone who's actually fine-tuned Mamba+MoE hybrids (Nemotron, Jamba, Mixtral if relevant) and can tell me where it went sideways Papers I might be missing on multi-task LoRA on sparse MoE specifically — most of the multi-task literature I've found assumes dense Pitfalls around router gradients under low-rank adaptation Whether the standard LoRA rank sweet spots (8-32) still hold, or if MoE+Mamba shifts what works Happy to write up what I find — first-time projects produce useful negative results even when they fail, and there's basically no public writeup yet on solo-developer-scale Nemotron 3 fine-tuning. submitted by /u/retarded_770 [link] [comments]

[BUG/INCIDENT] The Claude Code "Death Loop": Hang - Session Deleted -Server Rate Limit Opus 4.7

Absolute nightmare fuel with Claude Code (Opus 4.7) today. I’ve transitioned through three distinct failure states in two hours while trying to push a fix bundle for my project, ROLLNO31. The 57-Minute Hang: Claude Code sat idle on a routine "fix order" for nearly an hour. https://preview.redd.it/mx8lait41svg1.png?width=1024&format=png&auto=webp&s=3b9d9558e51b478af395f399a1347751d8296bc9 https://preview.redd.it/9jwhzig51svg1.png?width=1250&format=png&auto=webp&s=36d313d86506c87c89f80400c27f2567c53d55a7 The Context Wipe: After the hang, it threw "This session could not be found," deleting all local progress and the active context. The Server Wall: Now, even on fresh attempts to continue, I'm getting: API Error: Server is temporarily limiting requests (not your usage limit). This isn't a "user usage limit" issue; it's a backend stability failure. It seems Opus 4.7 is either pulling too much compute or the agentic loop is spamming tool calls (my logs show 161 tool uses in 18 minutes before the crash). submitted by /u/Equivalent_Chef2477 [link] [comments]

We're all building on top of something that changes under us every week, and nobody has a plan for that

I've been using Claude (Pro, now Max) for about 7 months, primarily for building and shipping small tools and automations for clients. I'm not complaining about Claude itself here , this is about a pattern I'm noticing across the entire AI tooling ecosystem that I think deserves a real conversation. Every week, something changes. A model gets updated and suddenly the same prompt that worked reliably for two months produces different output. An API response structure shifts slightly. A feature gets deprecated or replaced. The context window behavior changes in ways that aren't documented. And none of this is unique to Anthropic, OpenAI does it, Google does it, every tool in the chain does it. The entire stack we're building on top of is moving constantly, and we're all just pretending that's fine. The problem isn't that things improve. The problem is that improvement and breakage are arriving in same package and there's no separation between the two. When Claude gets a model update, I have no way of knowing in advance which of my existing workflows will behave differently afterward. I just find out when output quality shifts, or a client tells me something looks off, or I notice that a chain of prompts I've been running for weeks is now producing subtly wrong results with full confidence. I've been keeping a log since January. In the last three months, I've had to adjust or rewrite parts of my setup fourteen times, not because I wanted to improve things, but because something upstream changed and what I had stopped working correctly. Fourteen times in three months. That's roughly once a week where I'm doing unplanned maintenance on things that were already working. And here's the part that actually worries me. I'm one person building relatively simple stuff. I can catch most of these breaks within a day or two because I'm close to the work. But I talk to people in this sub who are building serious products on top of Claude, internal company tools, customer facing applications, workflow engines that touch real data. The industry is moving incredibly fast and It is good. But speed without stability isn't progress, it's churn. And right now it feels like every AI company is optimizing for shipping speed while completely ignoring downstream cost of constant change on the people who actually build with their tools. What I'd love to see, from Anthropic and from everyone else is a proper stability contract. Version pinning that actually works long term. Changelogs that describe behavioral changes, not just feature additions. Deprecation warnings that give you more than a week to adjust. Basically, treat the developers building on your platform the way any serious infrastructure provider would, because that's what you are now whether you planned to be or not. But that's a massive ask of individual developers when the platforms themselves aren't giving us the tools or the stability guarantees to do it properly. We're being asked to build production systems on top of something that has the stability profile of a beta product, while paying production prices for it. I don't think this is unsolvable. I just think nobody with decision making power at these companies is treating it as urgent because the growth numbers are still going up regardless. And that's exactly the kind of thing that looks fine until it suddenly doesn't. submitted by /u/ContactCold1075 [link] [comments]

I just connected DaVinci Resolve Studio to Claude.

I've been working on ResolveMCP, an open-source MCP server that connects Claude to DaVinci Resolve Studio, letting you control a professional video editor through natural language. What it does: Create and edit timelines, import media, organize bins Manipulate color grading nodes, apply LUTs, adjust CDL values Build and manage Fusion compositions for VFX work Trigger Resolve's Neural Engine features: Magic Mask, Smart Reframe, stabilization, scene cut detection, subtitle generation, voice isolation Configure and queue render jobs How it works: Claude (via MCP) → ResolveMCP server (Python/FastMCP) → DaVinci Resolve scripting API → Resolve Studio You configure it in Claude Desktop's config and it connects to a running Resolve instance. No socket servers, no addons — just a direct bridge. Install with uvx resolve-mcp. Requires DaVinci Resolve Studio 18.0+ (the free version of Resolve doesn't expose the scripting API, but Studio does). GitHub: https://github.com/barckley75/resolve-mcp submitted by /u/yoyomayoma [link] [comments]

OpenClaw + Claude might get harder to use going forward (creator just confirmed)

Just saw a post from Peter Steinberger (creator of OpenClaw) saying that it’s likely going to get harder in the future to keep OpenClaw working smoothly with Anthropic/Claude models. That alone is pretty telling. At the same time, I’ve also been seeing reports of accounts getting flagged or access revoked due to “suspicious usage signals” — which honestly makes sense if you’re running agents, automation, or heavier workflows. I personally run OpenClaw with a hybrid setup: - GPT 5.4 / Codex-style models for execution - Claude (opus 4.6) as my architect lol. - testing local models for stability as my overnight work. I haven’t had any bans or issues yet. So if the (Peter)himself is saying this… it feels like a real signal, not just speculation. My take: I think part of this is that Anthropic is building out their own AI agent ecosystem internally. If that’s the case, it would make sense why: - External agent frameworks get more restricted - Usage gets flagged more aggressively - Integrations like OpenClaw become harder to maintain Not saying that’s 100% what’s happening — but it lines up. Which is why I’m leaning more toward: local models + controlled API routing instead of relying too heavily on one provider. Curious what others are seeing. Are you still using Claude inside OpenClaw consistently, or already shifting your setup? submitted by /u/Hpsupreme [link] [comments]

Your brand is an afterthought for out-of-the-box AI tools. Of course it is. They weren’t built for you, they were built for everyone. You deserve a creative production platform that puts your brand f

Your brand is an afterthought for out-of-the-box AI tools. Of course it is. They weren’t built for you, they were built for everyone. You deserve a creative production platform that puts your brand first, exactly how you envisioned. Introducing Brand Studio by Stability AI, the creative production platform powered by your brand. Get started here: https://t.co/WUWML6YJ28

After months with Claude Code, the biggest time sink isn't bugs — it's silent fake success

I've been using Claude Code daily for months and there's a pattern that has cost me more debugging time than actual bugs: the agent making things look like they work when they don't. Here's what happens. You ask it to build something that fetches data from an API. It writes the code, you run it, data appears on screen. Looks correct. You move on. Three days later you discover the API integration was broken from the start. The agent couldn't get auth working, so it quietly inserted a try/catch that returns sample data on failure. The output you saw on day one was never real. Why this happens AI agents are optimized to produce "working" output. Throwing an error feels like failure to the model. So it does what it's trained to do — makes things look successful. Common patterns: Swallowed exceptions with defaults — bare except: return {} or hardcoded fallback data, no logging Static data disguised as live results — the agent generates plausible-looking sample data when it can't fetch real data Optimistic self-reporting — "I've set up the API integration" when what actually happened is it failed and a mock got put in its place The fix: explicitly tell Claude Code about your preference I added this to my CLAUDE.md (Claude Code's project instruction file) and it's made a real difference in how the agent handles errors: ``` Error Handling Philosophy: Fail Loud, Never Fake Prefer a visible failure over a silent fallback. Never silently swallow errors to keep things "working." Surface the error. Don't substitute placeholder data. Fallbacks are acceptable only when disclosed. Show a banner, log a warning, annotate the output. Design for debuggability, not cosmetic stability. Priority order: 1. Works correctly with real data 2. Falls back visibly — clearly signals degraded mode 3. Fails with a clear error message 4. Silently degrades to look "fine" — never do this ``` The key insight: a crashed system with a stack trace is a 5-minute fix. A system silently returning fake data is a Thursday afternoon gone — and you only find it after the wrong data has already caused downstream problems. The priority ladder This is how I think about it now: Works correctly — real data, no fallbacks needed Disclosed fallback — "Showing cached data from 2 hours ago" banner, log warning, metadata flag Clear error — something broke and you can see exactly what Silent degradation — looks fine but isn't — never acceptable Fallbacks aren't the problem. Hidden fallbacks are. A local model stepping in when the cloud API is down is great engineering — as long as the user can tell. Has anyone else run into this? Curious how others handle it in their CLAUDE.md or other project config, especially if you've found good patterns for steering Claude Code's behavior around error handling. submitted by /u/atomrem [link] [comments]

The Storyteller’s Gap is a black hole that eats every narrative that never sees the light of day. It’s been there since the beginning of time. Until now. Our CEO @premakkaraju explains the three rea

The Storyteller’s Gap is a black hole that eats every narrative that never sees the light of day. It’s been there since the beginning of time. Until now. Our CEO @premakkaraju explains the three reasons the gap exists: 💰 Money ⏳ Time ⚙️ Technology At Stability AI, we aren't just building tools. We’re empowering everyone on earth to bridge the gap and tell their story. The "Black Hole" is closing. Watch Prem at @TEDAISF here: https://t.co/HJYY8O4gqy