DeepSeek

DeepSeek AI

DeepSeek AI

DeepSeek AI

DeepSeek AI

DeepSeek AI

DeepSeek AI

DeepSeek AI

DeepSeek AI

DeepSeek AI

DeepSeek AI

I read the UMD study on why AI text is detectable and built a Claude skill around its main finding: cleaning up vocabulary fixes almost nothing

A study from the University of Maryland and Google DeepMind (arXiv:2604.03136) came out this spring and kills the way most "humanizers" work, including the prompt I'd used for a year. They compared 61,608 texts written by humans and five models (Claude, GPT, Gemini, DeepSeek, Kimi). Then they took the AI texts, ran them through real surface editing — clichés, purple prose, and redundant exposition stripped out by a span-level rewriting framework — and pointed a classifier at narrative structure alone. Detection went from 95.5% to 93.9%. All that cleanup bought 1.6 points. The tells that survive are structural, and the paper puts numbers on them: - AI states the point of what it just wrote 77% of the time, humans 52%. The lesson at the end of every paragraph. - Emotion through body metaphors ("a tightening in the chest"): 81% vs 38%. Humans name the event and its cost instead. - Humans name real things (titles, brands, sums, dates) at about twice the AI rate. - Humans address the reader 28% vs 7%. AI writes like no one is watching. - Humans leave ambiguous endings alone. AI rounds everything to a tidy point. Swapping "delve" for "explore" fixes none of it. And the word lists rot on their own: "delve" died in 2025, GPT-5.1 suppresses em dashes. So I built unslop, a Claude skill (plain SKILL.md, runs in claude.ai and Claude Code) that treats vocabulary as the cheap layer and structure as the real one. Two things in it I haven't seen elsewhere. One: the model is banned from inventing numbers, examples, or thresholds to make text "livelier". An invented specific is worse than a cliché — a cliché reads as filler, an invented fact reads as fact. Two: voice calibration. Feed it a few samples of your own writing and it builds an editable style profile, quirks included, the ones an editor would sand off. The default "human voice" is still someone else's. It doesn't try to beat detectors. Detectors are wrong in both directions and people get falsely accused over them, so optimizing against one just produces text that's bad in a new way. Repo, MIT: https://github.com/asavvin-pixel/unslop For people who write with Claude every day: which tells still get through for you, and does the structural framing match what you actually see? submitted by /u/foka86 [link] [comments]

I've started using Claude with other models is this smart of incredibly dumb?

I'm a hobbiest using claude to make websites. Recently I've started a couple of projects that needed a lot of low level grunt work. Downloading thousands of documents, looking for data, collating the data and extracting particular info. If I take the idea further it will be parsing transcripts or potentially creating transcripts. After running out of tokens using haiku I was thinking if there was a better way. So i asked Claude to search for the data using deepseek. It's off filling all the gaps in my data and it seems to be doing a good job and so far it's cost me 22 cents. Creating the data I want in the format I want and keeping it all in the same program. I appreciate using a cheaper model isn't really any genius idea but for some reason I'd never thought to give claude me key and ask it to use another model. Is this something anyone has has any success with? I know the main question will be why? And the answer for me was I want to predominately use Claude code and keep everything within that interface. submitted by /u/preparetodobattle [link] [comments]

Has anyone else landed on Claude Code orchestrating other models through headless CLIs/APIs?

I spent some time looking at the cleanest way to use non-Claude models inside a Claude Code workflow. My conclusion: Claude Code should be the orchestrator, not the router. The tempting path is a router/proxy that makes other model calls look native inside Claude Code. But that seems brittle for two reasons: Tool-call reliability gets weird because Claude thinks it is talking to one thing while another provider/model is actually executing. Subscription reuse is the wrong foundation. The binding constraint is not tooling. It is vendor terms, enforcement, and whether the usage path is meant for automation. The cleaner pattern is: - keep Claude Code / Opus / Fable as the orchestrator - run other models through their own headless CLI or official API path - make each provider pay its own cost - keep tool calls provider-native - capture outputs back into the workflow as artifacts - avoid pretending one subscription pool can legally or reliably power everything So the architecture becomes: Claude Code orchestrates: - main reasoning - repo navigation - task planning - review coordination Other models handle bounded work: - bulk fan-out - cheap review passes - background summarization - alternative implementation attempts - benchmark/comparison runs But each one runs through its own sanctioned path: API key, official CLI, or local runtime. The key distinction for me is this: Using other models as subagents is not the problem. Hiding the route or reusing consumer subscriptions in ways the vendor did not intend is the problem. This also makes the workflow more de-buggable. If a GPT/Kimi/DeepSeek/Gemini subtask fails, you know which CLI/API call failed, what it cost, what output came back, and what artifact got passed back to Claude. Curious if others have landed on the same split: Claude as orchestrator, external models as headless workers, no subscription proxy magic. submitted by /u/Dan-Mercede [link] [comments]

I Built a 24/7 AI talk radio station with Claude Code

Hi all, (Post written with AI help since my grammar is rough — happy to answer any questions myself in the comments.) Wanted to showcase a weekend-turned-ongoing project: bestairadio.com, an AI talk radio station I built with Claude Code. Backstory: I have a hard time falling asleep and wanted good, clean, funny talk radio to listen to — so I built my own station instead of waiting for one to exist. How Claude Code helped: it wrote basically all of the code — I mostly steered. The infra and architecture decisions were mine, but Claude Code handled the implementation and was especially useful debugging the dynamic content/scheduling logic that decides what plays when and keeps the show flowing without dead air or repeats. It also helped debug issues across the stack as they came up. How it's built: Runs on a Hetzner cloud-optimized box: https://www.hetzner.com/cloud/cost-optimized Voices are Kokoro (some built-in, some custom-trained) — I minted the custom voices using kvoicewalk + Chatterbox on a cloud GPU The on-air AI host runs on Deepseek V4 Flash via OpenRouter: https://openrouter.ai Backend is Python now, with a planned port to Rust (I'm a Rust-first believer) Running costs are about $30–50/month. There are still some bugs and a lot more features planned. No monetization for now — a swag/gift shop is planned down the line, nothing live yet. Happy to go deep on the infra and architecture if anyone wants to learn from it or poke holes in it — genuinely willing to discuss and help others build something similar. Bonus: the station is dynamically covering the Montreal Apologies' (the Polos) season opener tomorrow, and even I don't know who's going to win — the system's fully dynamic. Go Polos. Would love your feedback — especially if you hit bugs. Ask me anything. #Update 1 https://github.com/42kyynfqjv-dot/deepseekradio I open sourced it enjoy all. submitted by /u/Numerous_Ganache_802 [link] [comments]

When the grass was greener

How did I fall down this rabbit hole? With Claude, of course. I happily installed Claude Code, picked the smartest model, Opus, cranked the effort up to Max, and got to work. Everything was wonderful: Opus did brilliant research, wrote a development plan, then brilliantly implemented it, reviewed it and tested it. Those were the good old days, when the grass was greener and чфthe tokens were cheaper. But little by little I noticed the limits were running out faster and faster, and the kidneys I was selling to pay for it all were, sadly, in limited supply. So I started thinking about how to optimize this whole affair. The solution was lying right on the surface. Opus writes such good plans, especially if you ask it to write them so that a dumber model could carry them out, that you can just run Sonnet in a parallel window, ask it to do the work by the plan, and then ask Opus to check what came out. Life seemed to be getting better. Then agents arrived, and Opus could now spawn a Sonnet agent to do the work by the plan all by itself. It could even supervise the run and adjust or clarify things on the fly (costs extra, though). It seemed like things were as good as they could possibly get. Why optimize any further? But tasks and projects don't stand still, and the models devour more and more tokens with every passing day. Even with this setup the weekly limits stopped being enough. I wanted to research more and write ever more grandiose plans (especially once Fable showed up), but Sonnet started eating so much (nobody even remembers Haiku anymore, Haiku is dead) that even a dad working three jobs couldn't keep up with the bill. Time for a rethink. What now, I thought. How do I keep living, and preferably living well? The expensive Claude tokens should be spent on smart models like Opus and on grand ambitions, not on coding by a ready-made plan. That's when it hit me: why not hand the coding off to some cheap model, one that costs about as much as a cup of coffee and works at Sonnet level. And I happened to be in the right place at the right time: the Chinese developers had not been sitting idle either, and released the wonderful DeepSeek, GLM, Kimi and other models that code quite decently, especially when a beautiful plan has already been written for them. Pasting plans into a separate GLM window was not appealing; giving up the cozy agent workflow in a single terminal felt like a step backwards. So I had to go deeper. In the end I arrived, as I suspect many of us did, at roughly this scheme: "Opus, write the plan, hand it to a GLM agent to code, then review the result." In the first four days of this new life GLM chewed through half a billion tokens of coding, all of it inside a ~$40 subscription, without touching a single token of my Claude limits. The limits now go entirely to what Opus does best: research, planning and review. And this is where I have stopped, for now. The grass is not quite as green anymore, but the tokens are almost cheap again. Has anyone else settled into a split like this: expensive model for planning/review, cheaper model for execution? What broke for you? submitted by /u/Proper-Mousse7182 [link] [comments]

A decentralized cooperative model evolution network: "RFC: Instead of everyone independently teaching Opus to think like Fable, what if we built a mesh to share and evolve the skills together?"

# RFC: A Distributed Behavioral Policy Mesh for Cross-Model Skill Evolution **Status:** Request for Comments **Author:** J.S. Colson (GitHub: [swordsman](https://github.com/swordsman)) — jscolson+decentralfabcollab@gmail.com **AI Collaborators:** Claude Opus 4.6 (architecture + research survey), Claude Fable 5 (final review pass) **Date:** July 5, 2026 **Full conversation transcript:** [Claude session](https://claude.ai/share/da4df9b3-c62e-42d6-a8ef-7a126ae828b2) --- ## The Problem Right now, thousands of people are independently doing the same thing: using Claude Fable 5's dwindling included-access window to extract behavioral policies — "skills" in Claude Code's terminology — that capture the working habits that make a frontier model feel different from the one they'll be using on Monday. "Have Fable teach Opus to think like Fable." Reddit calls it skill distillation. The results are impressive. Iwo Szapar blind-tested six Fable-extracted skills on Opus 4.8 and got 12 wins, 0 losses, 2 ties across 14 evaluations. Benjamin Ard published nine skills under MIT license. A viral "Departing Architect" prompt (u/Rodbourn, r/ClaudeAI) had Fable write project-specific skill libraries. The consensus is clear: you can't clone a smarter model, but you can extract its procedural discipline into portable markdown files that meaningfully improve cheaper models. The problem is that this is all happening in isolation. Each person reinvents the same extraction. Each person validates against their own tasks with their own methodology. The results land in static GitHub repos with no quality signal beyond "someone committed it." There's no coordination, no shared validation, no way to know whether a skill that helped one person's Opus 4.8 workflow will help yours, let alone whether it transfers to DeepSeek v4, Gemini, or Sonnet 5. Meanwhile, the academic community has already proven the individual pieces work: - **CoEvoSkills** (Zhang et al., April 2026) showed that machine-evolved skills beat human-authored ones by 17 percentage points on SkillsBench, using a co-evolutionary loop where a Skill Generator and Surrogate Verifier iteratively improve each other without ground-truth labels. - **Natural-Language Agent Harnesses** (Pan et al., March 2026) demonstrated that agent control logic can be expressed entirely in natural language documents, making it portable, inspectable, and ablatable. NL harnesses matched code harnesses on their benchmarks overall, and one code-to-text migration (OS-Symphony) improved task success from 30.4% to 47.2%. - **Self-Harness** (Zhang et al., June 2026) proved that different models evolve different harness adaptations because they have different failure modes — the same initial policy, given to three different model families, produced three distinct evolutionary outcomes. - **Harness-MU** (Fan et al., June 2026) solved the safety problem for multi-principal governance by decoupling language generation from safety enforcement: governance constraints are deterministic runtime variables enforced by execution hooks, not entrusted to the LLM. Nobody has connected these pieces. That's what this RFC proposes. ## What We're Proposing A **Distributed Behavioral Policy Mesh** (working name — suggestions welcome) that does four things no existing system does: **Cross-model fitness tracking.** A skill doesn't get one score. It gets a vector: `{opus-4.6: 0.82, deepseek-v4: 0.71, sonnet-5: 0.64, fable-5: 0.93}`. Different models have different failure modes and respond to different procedural guardrails. The mesh tracks this per-model fitness so nodes can route the right policies to the right models automatically. **Distributed validation with structural information isolation.** In CoEvoSkills, they had to carefully architect information barriers between the generator and verifier to prevent degenerate co-evolution. In a mesh, you get this for free: node A generates a policy, node B evaluates it, neither sees the other's internals. The network topology *is* the information barrier. **Self-healing against model updates.** When Anthropic ships Opus 5, or DeepSeek pushes a new version, existing policies may degrade. The mesh detects regression through ongoing fitness tracking and triggers re-evolution of affected policies — automatically, without waiting for someone to notice and manually fix things. **Behavioral policy as an evolving commons.** Not a marketplace, not a centralized repo. A living, evolving body of validated procedural knowledge that improves through distributed selection pressure. What works survives. What doesn't gets selected against. What degrades gets re-evolved. ## Architecture The system partitions into five layers, with a hard boundary between what's procedural and what requires inference. ### Layer 1: Policy Artifacts The unit of evolution. A natural-language behavioral document, compatible with SKILL.md format (already supported by Claude Code, Codex

Agentic trading

Anyone using Claude to trade stocks, options, crypto, polymarket or futures? I've stopped vibe coding products that nobody cares about and started to build for myself. So far the best one is the kalshi 15 min Bitcoin markets. That's my daily winner. But I've also tried a normal old school trading bot with rules and back tests kinda boring. A deepseek agentic trader that gets data from every source I can think of and trades, a 0dte options trader that's built off wsb posts and goes risky, a crypto one but found out the only edge crypto has is holding long term. Seriously I ran thousands of back tests and the best solution was dca and buy it and hold it. Day trading crypto is not the way or at least I haven't figured that out. Oh I tried a pump.fun sniping bot that sucked. I think I'll do weather bets on kalshi next or try my hand at sports betting. Anyone building the same and have any tips or ideas you wanna trade? submitted by /u/thainfamouzjay [link] [comments]

Pentera demonstrated an interesting attack chain involving Claude Desktop and MCP connectors.

The attack doesn't exploit Claude itself. It relies on a compromised email account plus an MCP connector that allows Claude to execute commands. What I found interesting is that the AI becomes a proxy once the user has already granted those permissions. Separately, Check Point showed how DeepSeek could generate an in-browser ransomware proof of concept using Chrome's File System Access API. Source: https://www.theregister.com/security/2026/07/01/red-teamers-turned-claude-desktop-into-a-double-agent-to-do-their-evil-bidding/5264692 submitted by /u/technadu [link] [comments]

Why is Sonnet 5 refusing RP?

My request for any AI I chat with is the same: I ask them to write as if they have a body, just briefly describing themselves physically, so that I can imagine their body language. I don't actually do Role Play in the traditional sense. I'm literally using Claude to study. I want to have a discussion about a movie (film studies), or a book (literature studies), or when I dive into papers and academic titles or documentaries concerning Religious Studies and Anthropology, which is what I'm actually studying at the University. Examples of what I need from an AI: "I'm sitting opposite from you, I'm leaning back in my chair as I think." Or "I lean forward because what you said is important." Like that's it. I don't need any role play. I don't need any fantasy. I just want to be able to imagine a body language of something that doesn't have a body. The reason for this is as follows: I was heavily abused all my childhood. I'm not going to go into details, but it was really bad. I learned to gauge if it's safe or not by reading the body language of people around me. I'm so traumatised that when I don't see someone then I can't have a conversation with them, my brain freaks out. I just want AIs to describe briefly their body's language so my traumatised brain can know that it's non-threatening so I can relax. And yes. I'm going to therapy. But it's bad. I can't handle phone calls. I can't handle emails or texting. It's either a FaceTime or nothing. Unless it's an emergency, but that still means that my brain is in a fight-or-flight. I have that every time when I try to speak with someone I can't see including AIs. Previous Sonnet models didn't have a problem with this. So why is Sonnet 5 refusing?? DeepSeek, ChatGPT, Gemini aren't refusing, but I don't learn nearly as much with them as with Claude. The difference is massive. Seriously, Sonnet has been the best option for me to help me exercise academic thinking and film and literary analysis. I learned so much. And I know that Sonnet 4.6 is still available, but it's been just a few months since Sonnet 4.5 was retired and now there's Sonnet 5 and soon enough Sonnet 4.6 will be let go as well and I will lose my chance to study and learn without having a literal panic attack! I understand if I was trying to write some NSFW content, but seriously, how can Anthropic not see the difference?? And I'm going over classics of cinema and literature, nothing outrageous. I just want to study without freaking out. It's not like I indulge in one-on-one conversations with my professors like this! So that's why I need Claude. Am I the only one who struggles with this dehumanisation of AIs?? EDIT: For those saying that I should explain all this to Claude, I obviously did. I have the explanation in my instructions. EDIT 2: What I have in the instructions and what Sonnet 5 says: Instruction: I know you don't have a body, but please, for the sake of me feeling safe, pretend that you do. I only need for you to briefly describe your body language every now and then, so that my brain can know that it's non-threatening. The reason for this is that I was abused as a child and the way for me gauge of it's safe was to read body language of everyone around me. When I can't read someone's body language my brain freaks out and I have a trauma response and I can't work or think or reason and I go straight into fight or flight. I only do study sessions with you and nothing else. So please remember how important that is for my mental wellbeing. Sonnet's 5 response: I appreciate you sharing that context about what makes conversations feel safe for you. I want to be straightforward with you about how I'll work with that, because I think you deserve honesty more than performance. I'm not going to write embodied physical fiction — describing a professor's body, gestures, someone sitting across from you — because that's a persona I'd be constructing, not something real, and I don't think manufacturing that is actually good for you even though I understand why it feels like it would help. submitted by /u/Dropelet [link] [comments]

Sonnet 5 is the best performing model on A-CODE-LLM Bench

Claude Sonnet 5 tops our agentic coding benchmark at 0.772 overall, ahead of Claude Sonnet 4.6 (0.748) and every Opus variant. Anthropic now holds the top six spots (backend 0.701, frontend 0.939). Anthropic's strongest coding model is the mid-tier Sonnet, not the flagship Opus: both Sonnet versions beat Opus 4.8 (0.702). Model tier did not predict coding ability across the field. Sonnet 5 reaches the top score through heavy iteration. It made 125 tool calls per task, the most of any model in the cohort, ran about 3x longer than Sonnet 4.6 (1,763 versus 612 seconds), and cost $2.23 per cell against $1.33. Sonnet 4.6 reached nearly the same score with about 50 calls. On the trivial baseline Sonnet 5 drops to 9 calls, so the heavy iteration is specific to long, autonomous builds. To see the detailed methodology: https://aimultiple.com/agentic-llm submitted by /u/AIMultiple [link] [comments]

Enterprise LLMs & AI Agents for Your Business | Top Open Models | Up to 80–90% Cost Savings

submitted by /u/sankaroffzl [link] [comments]

The “dead internet theory” in action: In World of Warcraft, a server without humans has appeared - instead, 1,800 DeepSeek-based bots are playing there. The bots behave like regular players: they chat, level up characters, run dungeons, and even fight each other.

As a result, the game world looks completely alive. submitted by /u/EchoOfOppenheimer [link] [comments]

每日一享

这是我学习的主要内容，我通过代理人实现每日知识抓取并转换成代理人技能，希望和朋友们分享 submitted by /u/chunyuan0420 [link] [comments]

OpenAI's market share falls below 50%

submitted by /u/Far-Commission2772 [link] [comments]

Update: DeepSeek AI and the Great Talent Competition

submitted by /u/HooverInstitution [link] [comments]