标签：#art

MiniMax released M3 today and the API is already live. Worth separating what comes from their own official model page versus what comes from the launch announcement, because some of the numbers are sourced differently. From the official model page: BrowseComp 83.5, ahead of Claude Opus 4.7 at 79.3. PostTrainBench 37.1, which ranks third behind Opus 4.7 at 42.4 and GPT-5.5 at 39.3. From the launch announcement: SWE-Bench Pro 59.0%, Terminal Bench 2.1 66.0%, MCP Atlas 74.2%. The headline "beats Opus" is BrowseComp-specific, not a general capability claim across all dimensions. The context window is up to 1M tokens, implemented through their in-house MiniMax Sparse Attention architecture. They state 512K as the guaranteed minimum with 1M as the ceiling. The model was trained on 100T+ tokens and is natively multimodal rather than vision being added after the fact. Open-weights release is coming to HuggingFace and GitHub but listed as "coming soon." API access is available now through several paths, including OpenAI-compatible endpoints, while the weights are still pending. The model also supports native MCP tooling, which is where the 74.2% MCP Atlas number comes from. The demo claims are the part worth being skeptical about. A 12-hour autonomous ICLR paper replication run and a CUDA kernel optimization loop reaching 9.4x speedup are impressive if real, but these are curated showcase demos that are hard to evaluate from a screenshot. Whether sparse attention holds up at 900K+ tokens in practice rather than in controlled benchmarks is an open question. submitted by /u/Drysetcat [link] [留言]

Every impressive agent demo skips the same three things: Auth. The demo target is open. The real one has a login and a 2FA prompt. Identity. The demo agent acts as the developer. The real one needs its own email, accounts, and a place to keep secrets. State. The demo is one clean run. The real one has to remember what it did last time and resume. These are not AI problems, which is exactly why they get skipped in AI demos. But they are most of the work to go from "cool clip" to "thing that runs unattended." The model is increasingly the easy part. The unglamorous identity-and-state layer around it is where products actually live or die. Curious whether people think this layer gets commoditized into the foundation models, or stays a separate thing you assemble. submitted by /u/kumard3 [link] [留言]

[ Removed by Reddit on account of violating the content policy . ] submitted by /u/n4r735 [link] [留言]

Operator perspective. I use AI daily across three companies and I am bullish on it, but the gap between what gets shouted on stage and what the data shows is enormous. Best measured number across hundreds of engineers is about 7.8%, and 66% of the people who hit a peak gain saw it fade the next quarter. At the same time, people are being pushed onto it under threat of their jobs while the return is not even proven to the people mandating it. My read is the anger is not really “AI is bad,” it is “my boss profits from me using it and I do not.” Where do you land - is the resistance cognitive (it erodes skill) or economic (the gain is not shared)? submitted by /u/Alternative_Letter72 [link] [留言]

I’ve been using AI pretty heavily for the past few months — quick research, rewriting emails, brainstorming ideas, even helping outline stuff I need to write. It saves so much time and the output is usually decent. But lately I’ve noticed something weird: I’m second-guessing myself way less. I’ll get an answer from it and just kind of roll with it instead of thinking it through like I used to. Yesterday I asked it about something I already had a rough opinion on, accepted its take, and only later realized I didn’t even challenge any part of it. It feels convenient as hell, but also a little unsettling. Like I’m outsourcing the actual thinking part. Is this normal? Or am I slowly losing the habit of thinking deeply on my own? Anyone else feeling this? submitted by /u/pen-pineapple-apple [link] [留言]

Online it feels like every company is fully embracing AI. In reality, most organizations I interact with are still trying to figure out where it fits into existing workflows, processes and software. The interesting conversations aren't usually about models anymore. They're about trust, reliability, permissions, governance and how AI fits into the way people already work. The gap between AI demos and real-world adoption still feels larger than most people realize. submitted by /u/Bladerunner_7_ [link] [留言]

Smart Lighting Protocol Showdown: Zigbee vs Matter vs BLE Mesh (2026) After deploying thousands of Zigbee smart lights through our manufacturing line at nexLAMP, and watching countless customers struggle with protocol selection, I decided to write this practical comparison. The Real Problem "My smart lights keep disconnecting! I think I chose the wrong protocol..." This is the #1 complaint I see on Reddit, Xiaohongshu, and Zhihu. The fix isn't a better router — it's choosing the right protocol from day one. Protocol Deep Dive Zigbee — The Workhorse Frequency : 2.4 GHz (separate from WiFi) Topology : Star + Mesh hybrid Max devices : 200+ per coordinator Latency : 50-200ms Cost/unit : ~$3.5-5.0 (Tuya Zigbee drivers) Why it wins for lighting: Each node is a repeater → self-healing mesh Ultra-low power → years on coin cell for sensors Mature ecosystem → Tuya, Hue, Aqara, Xiaomi all ship Zigbee The catch: You need a Zigbee gateway (~$15-20). This is the only upfront cost. BLE Mesh — The Budget Option Frequency : 2.4 GHz (shared with WiFi/BLE) Topology : Managed flood mesh Max devices : ~50 (practical limit ~30) Latency : 100-500ms (increases with node count) Cost/unit : ~$2.0-3.5 The flooding problem: Every command is broadcast to every node. With N nodes, you get O(N²) message propagation. Past 30 devices, you'll notice visible lag. Good for: Small apartments (≤ 6 lights), budget projects. Matter — The Future Transport : Thread (preferred) or WiFi Topology : Thread mesh (similar to Zigbee) Max devices : 250+ (theoretical) Latency : 30-150ms (Thread), variable (WiFi) Cost/unit : ~$7.0-11.0 (currently higher) Matter's promise is genuine cross-platform control. But in 2026: Pros: Native HomeKit, Alexa, Google Home support Thread mesh is excellent (when it works) IP-based → easier cloud integration Cons: Thread Border Routers aren't ubiquitous yet Advanced lighting features still evolving Premium pricing for early adoption Cost Analysis (20-Fixture Deployment) Protocol Driv

Thoughts? Do you guys use models like Kimi or DeepSeek? Are you worried about data privacy, or not so much concern? submitted by /u/RutabagaTechnical822 [link] [留言]

I found a site like this a while ago but I can’t seem to find it now. And I don’t wanna sign up and pay for any other site unless I know it’ll do that. Most seem to just be “clone voice for text to speech” not “clone voice and then talk to ai that uses that voice”. I need the latter submitted by /u/OkWatermelonlesson65 [link] [留言]

I've been building Linkwise as a solo developer for the past year. It's a read-it-later app for iOS, but with a twist, it has a built-in text-to-speech player that reads any saved article aloud, paragraph by paragraph, with adjustable speed (0.8x to 2.5x). I built it because I kept saving articles I'd never get back to. Now I just listen to them on walks or during my commute. Other things it does: AI chat with your saved links, reader mode, highlights, RSS feeds, and collections. Would love to hear what you think. Roast it, break it, suggest features, all welcome. submitted by /u/dheeraj_iosdev [link] [留言]

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

The problem I wanted to solve: Stockfish tells you what the best move is, but never why . Players under 1800 don't lose because they can't read centipawns — they lose because they don't understand plans, structures, key squares. What the tool does: Imports your games from Chess.com or Lichess Stockfish 17.1 WASM runs in your browser (fully local, nothing uploaded) A pattern detector finds 18 types of recurring mistakes across all your games (missed forks, exposed king, bad bishop, neglected development...) An LLM generates coaching narratives in the style of a 2700+ coach Instead of: -89 cp · Best: Nc3 Nf6 Be3 The AI coach says: "Bd3 is premature — the bishop attacks nothing and blocks d3 where the queen may want to go. Nc3 was the right move: it defends d4, prevents Black's ...e5 counterplay, and leaves the bishop free to settle on Be3 or Be2 depending on Black's plan." You can also chat with the coach — it knows your full game history, opening stats, specific weaknesses. Ask "why do I keep losing with Black in the French?" and it answers with data from YOUR games. Other features: spaced repetition (SM-2) on your own blunders, puzzle rush with real mistakes, 6-month progress tracking. Free tier: unlimited Stockfish. Pro ($14.99/mo, 15-day free trial): LLM coach + chat. https://chessmentorai.com Happy to discuss the prompting approach — getting the LLM to explain chess like a coach (not an engine) was the hardest part. submitted by /u/sepiropht [link] [留言]

Most security tools for AI agents check one message at a time. Arc Gate tracks the whole conversation. That matters because the attacks that actually work in production don’t happen in one message. They happen across 8 turns. Each one looks clean. By the time the payload arrives your agent is already primed to execute it. I built Arc Gate using a geometric framework from my own research to detect adversarial behavioral drift across a full session — not just flag individual messages. When a conversation starts drifting toward something dangerous, it catches the pattern before the attack completes. I’m looking for 3 teams running real agents to test it against actual workflows and tell me where it breaks. Not chatbot wrappers. Agents with real tool access. Browser use, email actions, MCP servers, internal copilots, workflow automation. No charge. No sales call. Just feedback from people close to production. Comment or DM me if that’s you. Platform: https://bendexgeometry.com GitHub: https://github.com/9hannahnine-jpg/arc-gate Demo: https://web-production-6e47f.up.railway.app/demo submitted by /u/Turbulent-Tap6723 [link] [留言]

Hot take: if I wanted to gather data via the internet, and I’m writing scripts/code to speed up the process, I have to follow some basic rules (ie look at the sitemap, find relevant robots.txt, follow that websites preference and rules). But it seems any AI-agent I’ve used does not give af about rules and limits, and is totally cool building me a scraper that will perform hundreds of thousands of requests without regards to the website owner’s preference. Given it’s widely known you can use AI for simple coding tasks I can easily see a future where ordinary individuals are operating their own scrapers. Especially in gathering high-value information that “seems easy to get” like google search rankings, or job data. This creates an obvious nightmare for Google, ATS platforms, and just about every website on the internet if everyone and their mother starts spinning up Playwright sessions in Python. I’m deadset on this being a responsbility of AI providers (anthropic, open ai, anysphere, etc). But how are these companies supposed to balance this without implementing guardrails that heavily limit their products? Maybe this has been solved and someone can feed my curiosity. submitted by /u/TacoTuesdayX [link] [留言]

The cybersecurity company is nearing a $300 million round led by Evolution Equity Partners.

For the AI artists here who like creating painterly / impasto style work, what generators are you guys using? Just curious since I'm much more into that theme as opposed to realism, but it feels like most generators cater to realism now. submitted by /u/geekedprompts [link] [留言]

Running multi-turn or multi-agent AI sessions? There is a consistent degradation pattern across tools: context fills with repeated history, tool schemas, and subagent handoffs. A 2026 paper by Bai et al. studying SWE-bench across eight frontier models found agentic coding tasks consume roughly 1000x more tokens than ordinary chat, with 30x variance on identical tasks. Accuracy does not rise with spend. In one tracked research synthesis run I observed context hit 450,000 tokens. The agent dropped early constraints, re-queried sources already in history, and required manual reset. After adding three controls, the same class of task peaked near 85,000 tokens: PLAN.md and INVARIANTS.md outside the conversation window, read fresh each major turn A 2,000-line read budget gate per turn (agent states intent before any retrieval) Out-of-band notes for subagent coordination so side traffic never enters the main transcript Dynamic tool discovery produces similar ratios. One harness reduced input tokens 96% and total spend 90% by loading schemas only for tools the agent actually selects, rather than injecting a full catalog on every call. Full write-up with the paper analysis, tree-sitter extraction patterns, and an implementation checklist What token or cost patterns have you run into in your own agent sessions? submitted by /u/magicroot75 [link] [留言]

Microsoft missed the boat on apps, so get ready for agents.

submitted by /u/sizelrd [link] [留言]