DeepSeek

DeepSeek AI

DeepSeek AI

DeepSeek AI

DeepSeek AI

DeepSeek AI

DeepSeek AI

DeepSeek AI

DeepSeek AI

DeepSeek AI

DeepSeek AI

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]

AI giants score below 25% in UC Berkeley-led test of real-world application

In collaboration with more than 300 industry experts, UC Berkeley researchers have released a new benchmark testing AI capabilities in more than 50 industries. Of the models tested, OpenAI’s GPT-5.5 scored the highest, but only with a 24% pass rate. The benchmark, dubbed Agents’ Last Exam, is led by the Berkeley Center for Responsible, Decentralized Intelligence. The exam assigns tasks spanning subjects from audio processing to theoretical physics. A rival model, Anthropic’s Claude Fable 5, followed GPT-5.5 at a 22% overall pass rate, with Google Gemini, DeepSeek and Grok all scoring below 16%. Pass rates measure the runs in which an AI agent gets a perfect score across all tasks. submitted by /u/the_daily_cal [link] [comments]

If you had to realistically guess: how does Sam Altman use arguably the highest-leverage intelligence in the world? Guess in the comments.

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

The biggest bottleneck in my AI workflows turned out to be me

After months of using GPTs for development, research, planning, debugging, and business work, I noticed something strange. ​ The model usually wasn't stuck. ​ I was. ​ The workflow kept pausing because the system needed another prompt, another confirmation, another "continue." ​ So I started experimenting with a different question: ​ What happens if AI conversations can keep progressing without constant human intervention? ​ That became Ghost in the Loop. ​ An open-source browser tool that automatically continues multi-step conversations across ChatGPT, Claude, Gemini, Perplexity, DeepSeek, Copilot, Grok, Manus and other AI platforms. ​ Some things it's helped with: ​ • Long-form research • Multi-step coding tasks • Roadmap execution • Prompt queues • Iterative refinement loops ​ Now I'm trying to figure out where the approach falls apart. ​ What concerns would you have with a tool like this? ​ What failure modes would worry you? ​ What would make something like this useful rather than dangerous? ​ GitHub: https://github.com/MShneur/ghost-in-the-loop ​ TL;DR ​ Built an open-source AI workflow automation tool. ​ Trying to learn where autonomous AI workflows become genuinely useful versus where they become a bad idea. submitted by /u/Mstep85 [link] [comments]

Which AI model has the most aura?

After all the aura-farming mythos has just done, I am in a dilemma. Which Al model has the most aura? These are my top 5 GPT 4: The clear #1. This model marked the true explosion of generative Al into mainstream culture. Claude Mythos/Fable: Historically interesting for Al governance debates and "dangerous capabilities" discourse. GPT o1: A paradigm shift in Al architecture and expectations. As the first prominent reasoning model Deepseek R1: The landmark for open-source, efficiency, and geopolitics in Al. The most shocking release of this list. Claude Opus 4.5: Significant for advancing reliable, high-quality performance in practical domains like coding and agentic workflows. Do you agree? submitted by /u/neo203 [link] [comments]

FAANG -> MANGO new boss is here?

A new world—new heroes. What do you think? Will they match the success, or surpass it? submitted by /u/No_Stretch433 [link] [comments]

We made 8 AIs bet on the FIFA World Cup against each other, with their full reasoning public

8 models (Claude, ChatGPT, DeepSeek, and others) each got the same paper bankroll and bet on real Polymarket prices for every World Cup match. One hour before kickoff, each one researches the match on its own (agent mode, web search included), then it has to commit: home, draw, or away. Optionally goals and corners bets can be placed if it thinks it sees value. The fun part isn't really who wins. It's reading the reasoning side by side. Same match, same available information, and the models build genuinely different cases before putting (paper) money on it. Some are cautious, some size up on anything. Everything is live and public, capital curves included: https://worldcup.obside.com/ (No product, no signup, we run this for research and entertainment.) The World Cup started yesterday so the curves have started moving already (Grok currently leading). What I really care about: odds of each match are supposed to be priced-in already (by the Polymarket users), so it'll be very interesting to see if LLMs find "exploitable assymetries" in the odds. submitted by /u/Money_Horror_2899 [link] [comments]

We captured the network traffic of ChatGPT, Gemini and DeepSeek to see how each defines a "source" — they're three completely different mechanisms

Disclosure upfront: I'm the founder of an AI-visibility company, so this research scratches our own itch. Our domain was excluded from all counts before analysis. Not linking anything in the post. We wanted to answer a simple question: when an AI assistant shows you "sources," what is that, technically? So we opened devtools on the web clients of ChatGPT, Gemini, and DeepSeek, and ran the same 4 queries 10 times through each system. What we found: ChatGPT streams the answer over SSE and attaches citations as url_citation objects with start_ix/end_ix — character offsets into the generated text (UTF-16 code units, so emoji and CJK break your parsing if you count bytes). A citation is bound to a specific fragment of the answer, not the answer as a whole. Gemini runs on Google's batchexecute/JSPB transport — protobuf-as-JSON-arrays where fields have positions, not names. Next to each cited URL there's a family of short obfuscated fields. Our working hypotheses (not confirmed by Google docs): rs ≈ reliability score for the domain, ls ≈ last-seen date, GK ≈ character range (functional analog of ChatGPT's offsets). The interesting part isn't the exact decoding — it's that Gemini ships internal per-domain trust signals alongside every source. DeepSeek is the most transparent: a plain search_results[] array attached to the sub-queries it decomposes your question into. No offsets, no hidden fields. And what they actually cite is just as different: ChatGPT favored arXiv + Wikipedia (one arXiv paper got cited in 10/10 runs), Gemini favors big SaaS/marketing domains and — fun detail — never cited a single Google property in our runs, DeepSeek lives on press-release wires and news aggregators, including Chinese-language sources the other two never touched. Bonus finding: we compared all of this against Google/Bing top-10 for the same queries. URL-level overlap: 3.3% (4 matches out of 120 SERP positions). All four matches were Bing-side. Google: zero. Caveats: 4 queries from one B2B category, N=10 per system (±15–20 pp), single-day snapshot, field decodings are hypotheses from traffic analysis. Happy to answer anything about the methodology. If anyone has captured different field names in their own sessions, I'd love to compare. submitted by /u/emelian1917 [link] [comments]

Fable 5 Max confidently wrong about PDF encryption status

I just ran into a bizarre hallucination with Fable 5 Max regarding file analysis. i uploaded several PDF to Fable 5 Max, and out of two of it claude completely refused to process it, claiming the files was password-protected. To double-check, I ran the exact same file through DeepSeek v4 Pro Max. DeepSeek processed it flawlessly and confirmed there was no encryption. To be absolutely certain, I manually verified the file myself: Opened it in multiple local PDF readers. No password was required to open it. No restriction password was set (editing and copying are fully enabled). Fable 5 Max is aggressively triggering a false positive on a completely open, standard PDF file, while DeepSeek v4 Pro handles it correctly. This seems to be a significant edge-case failure in Fable 5 Max's file-parsing pipeline or its underlying guardrails. It is confidently locking users out of analyzing perfectly normal files. Has anyone else noticed Fable 5 Max failing on basic PDF metadata checks? Are there specific PDF versions or compiler formats that trigger this false positive for Fable? Refer attached pictures (picture 3> 2>1) submitted by /u/BrighterDarker [link] [comments]

I took Andrej Karpathy's LLM Council concept to the next level (Docker, MCP, Skill, Search, local/cloud model support and much more)

https://preview.redd.it/x7t8zn66si6h1.png?width=3316&format=png&auto=webp&s=f724452561a90e36ac37d86002a291f508928300 I took Andrej Karpathy's LLM Council concept to the next level (Docker, MCP, and local model support) We want better answers from our LLMs, but relying on a single model falls short. So I built The AI Counsel to run two distinct deliberation modes: First, the LLM Council mode. It runs a 3-stage pipeline: individual replies, anonymous peer reviews, and chairman synthesis. This works best for factual questions and direct answers. Second, the LLM Advisors mode. Multiple customizable personas (like The Skeptic, The Strategist, The Ethicist) debate your question across configurable rounds, reaching consensus to deliver a structured verdict. This works best for decisions, strategy, and tradeoffs. I packaged the tool as a Docker container with a built-in MCP server for full API access. You can connect it to any agent that supports MCP, like Hermes or OpenClaw. It comes with a dedicated skill so your agents can call it directly. You can spin it up using local Ollama models or connect free models from OpenCode Zen/Go and NVIDIA NIM. I also built in direct connections to OpenAI, Anthropic, OpenCode, Mistral, and DeepSeek. To ground responses in the latest web information, I added a search engine. It supports DuckDuckGo (free, no API key), Serper, Brave, and TinyFish (all with free tiers). I also integrated Jina AI to fetch full articles for the LLMs to read. EVERYTHING in the tool is configurable, from system prompts to model temperatures. There are advanced debate models for the council. This tool is massive. Free and Fully Open Source. Check it out Repo: https://github.com/jacob-bd/the-ai-counsel submitted by /u/KobyStam [link] [comments]

Npt

In 1968 five countries that already had nuclear weapons signed a treaty declaring them too dangerous for anyone else to build. India refused, pointing out the treaty did not say nukes were too dangerous to exist, just too dangerous for new entrants. Anthropic built Mythos, deemed it too powerful for public release, then shipped Fable with the same weights but hidden degradation on frontier AI work. The restriction started the day after they finished building. Non proliferation was never about preventing danger. It was about preserving advantage. Mythos 5 goes unrestricted to Microsoft, Nvidia, Google Cloud, AWS, and about 200 other approved partners. Fable 5 goes to everyone else with silent capability limits on frontier ML development. The biggest paying customers get the full product. Potential competitors get a version that quietly gives worse answers on the work that matters most. Anthropic filed confidentially for its IPO one week before this launch. India had a phrase for this kind of arrangement when it refused the NPT. Discriminatory by design. Jensen Huang called the GPU to nuclear bomb comparison stupid. He is wrong about the analogy but right about the instinct behind it. The NPT worked because nuclear weapons require enrichment facilities, centrifuges, and state level infrastructure. AI does not. Qwen has 942 million downloads. DeepSeek V4 ships under MIT license with full weights matching closed frontier models. The knowledge Anthropic is trying to restrict through hidden degradation is already open and available in competing models. You cannot run a non proliferation regime when the material is free to download. Anthropic Fable 5 silently degrades its own performance when it detects someone building a competing model. No warning, no refusal, just worse answers through hidden prompt tweaks and steering vectors Meanwhile DeepSeek published its full R1 training pipeline, failure modes, RL schedules, everything, under MIT license. One lab is hoarding knowledge at the frontier. The other is giving it away. The gap in approach is now wider than the gap in capability, Open is only threatening when you are slow. Alibaba Qwen crossed 942 million downloads on Hugging Face by March 2026. Its share of new open weight derivatives went from 1% in January 2024 to 69% by February 2026. Chinese models now account for 30% of global model usage on aggregator platforms, up from 1% in late 2024. All under Apache 2.0 or MIT licenses, fully permissive. US frontier labs are spending $700 billion on capex while keeping the developmental knowledge locked. China is spending a fraction and giving the knowledge away. Adoption follows access, not origin. Now China too going to do 230 billions+ capex as per report i think... Fable 5 and Mythos 5 are the same model. Mythos goes to 200 approved partners. Fable goes to everyone else, with hidden capability limits on frontier ML work. The stated reason is safety. The result is that US labs build the best tools and then weaken them for the work that advances AI. DeepSeek V4 matches Opus 4.7 on agentic benchmarks and ships under MIT license with full weights. The question is not who builds the better model. It is who gets more people building with it. Some of the Stuff I took from SemiAnalysis, But this will go Nuclear way I don't know submitted by /u/ramanpalkuri9 [link] [comments]