📰 MK Tech Information

Free & Open Source Build better prompts. Every time. A rule-based prompt engineering wizard that teaches you why each field matters — not a black box that wraps an AI call. Start building See an example Step 1 Pick your goal. ✍️ Writing Blog posts, emails, stories, docs 💻 Coding Debug, write, review, explain code 🎨 Image Gen AI art prompts, product photos 🎥 Video Gen Sora, Runway, AI video prompts 📊 Data Analysis, SQL, visualization 🔬 Research Papers, literature, concepts 📢 Marketing Ads, landing pages, social posts ⚙️ Custom Build from scratch Framework ? RTF · Role—Task—Format CO-STAR · Context—Objective—Style—Tone—Audience—Response TAG · Task—Action—Goal APE · Action—Purpose—Expectation The simplest and most versatile framework. Define who the AI should be, what it should do, and how it should format the answer. Target Model ? Generic / Any ChatGPT Claude Gemini Clean labeled sections — works well with any AI model. Step 2 Fill in your prompt. Templates Reset Role / Persona ? Context / Background ? Task Required ? Output Format ? Tone / Style ? Constraints ? Examples (Few-Shot) Optional ? Live Preview Generic / Any Copy .txt Save Start typing to see your prompt here… Why this works 🏗️ The RTF framework (Role-Task-Format) gives you the three most impactful prompt levers in the simplest structure. ✨ Start filling in the fields above to see how each one strengthens your prompt. Every field you add reduces ambiguity. AI Refine Optional Configure an AI Provider in Settings to e

Nine cents. Two hundred and ninety dollars and twelve cents. Those are both the price of one million output tokens. Same unit, same dataset, read off vendor pages in the same week. The first is gpt-oss-120b on a single MI355X. The second is Qwen3.5 on eight H100s. Neither is a typo and neither is a trick. And almost none of the gap between them is the model. So I went looking for what it actually is. Here is what I found, measured across 24 providers and 378 GPU rental rates, ranked smallest to largest: Five levers. The two everyone talks about are the two smallest. This post walks up the list. The spread between the best and worst case for each measured lever. First, the question everyone actually asks Is self-hosting Running model inference on compute you rent or own instead of buying each request from a hosted model API. cheaper than the API? Here is the like-for-like comparison. Same weights, same model id, cheapest published option on each side. Cheapest published option, dollars per 1M output tokens Open model Cheapest hosted API Self-host @90% @60% @30% DeepSeek-R1-0528 $2.15 $0.88 $1.32 $2.63 MiniMax-M3 $1.20 $0.31 $0.47 $0.93 Llama-3.3-70B $0.40 $0.82 $1.24 $2.47 Hosted API versus self-hosted cost at three utilization assumptions. At 90% utilization The fraction of paid accelerator time during which the hardware is doing useful inference work. self-hosting takes two of three. At 30%, which is generous for most real deployments, the API takes two of three. So the answ

A s I typed in the words, “First Indian leader to visit the Prambanan temple”, in Google search for an article this week, the answer flashed Prime Minister Narendra Modi is the first Indian leader to visit the historic temple . However, I felt a nagging sense of doubt as India and Indonesia have collaborated since the 1940s, both militarily and culturally. To verify, I checked The Hindu’ s semantic search which browses through newspaper archives spanning over a century. As reported by The Hindu , on December 14, 1958, India’s President Dr. Rajendra Prasad drove down to Prambanan temple during his official visit to Indonesia. The article titled ‘Monument of Buddha’ noted how Dr. Prasad was impressed by the sight and that he had paid tribute to the renovation work, which was underway with the help of the Archaeological Survey of India. I asked the same question to various Artificial Intelligence (AI) tools such as ChatGPT, Gemini and Grok, and got the same answer — that Mr. Modi was the first Indian leader to visit the shrine. Prambanan Temple: Why is India restoring Indonesia’s largest Shiva temple? | Explained To further analyse the extent of AI’s handicap, I questioned both ChatGPT and Gemini about the “first Indian Prime Minister to visit Palestine”; it answered — Narendra Modi on February 10, 2018, when he visited Ramallah in the West Bank and held talks with Palestinian President Mahmoud Abbas . However, a look into The Hindu’s archives shows that Jawaharlal Nehru was the

3D Animated Portrait Nano Banana 2 3D Animated Portrait Portrait photo → a premium 3D animated avatar — same identity, sculpted features, soft studio lighting.

x Taboo “equilibrium”: Less confused frames for research on AI bargaining — LessWrong Safe Pareto Improvements Game Theory Open Source Game Theory AI World Modeling Frontpage 25 Taboo “equilibrium”: Less confused frames for research on AI bargaining by Anthony DiGiovanni 31st Jul 2026 12 min read 0 25 To understand why powerful AIs might get into conflict, and ways to mitigate it, we need to understand bargaining problems : situations where multiple agents have different preferences over Pareto-efficient outcomes. I’ve come to suspect that certain common frames on bargaining problems are confused. Here, I’ll explain why, and which frames I think are better. One motivation for this is to hopefully help others make progress in research on safe Pareto improvements (SPIs) , which are among the most promising approaches to mitigating the downsides of AI conflict, in my view. In this post I’ll: give some relevant background from my previous writings; ( more ) introduce mechanistic explanations as a crucial frame for AI bargaining research, using, as a running example, explanations of failure to coordinate demands in bargaining (this example also motivates the following bullets’ claims); ( more ) argue that instead of asking what would happen in Nash equilibrium, we should ask what kinds of beliefs AI bargainers would plausibly have about each other; ( more ) and argue that instead of relying much on heuristics about (e.g.) what agents would consider “exploitative”, we should carefu

Contents The internet ran out The mechanism, which is almost insultingly simple Which makes the whole map predictable in advance "But models can make their own training data" The genuinely bleak part Three ways to misread the table Can you cheat your way off the slow side? What would blow this up Here is a pattern that looks completely arbitrary until you find the single rule underneath it, at which point it stops looking arbitrary forever. AI is now superhuman at competitive programming. Superhuman at formal mathematics. Better than most professionals at finding software vulnerabilities. It plays board games at a level no human being will ever reach again, and nobody even finds that remarkable anymore. AI is also, after all of that, roughly fine at telling you whether to take the job offer. Fine at sensing when a negotiation has quietly turned against you. Fine at taste. The obvious explanation is that the first list is easy and the second list is hard. That explanation feels correct and is completely wrong. Competitive programming is not easier than having good judgment about people. Ask literally any programmer. The real rule is stranger, and once you see it you cannot stop seeing it: AI becomes superhuman at anything where checking the answer is cheap, and stays mediocre at everything where checking the answer is expensive. Not doing. Checking. To see why, we have to go back to the moment the industry ran out of internet. The internet ran out # For several years the recip

문샷 AI가 알리바바 그룹으로부터 2만개의 엔비디아 GPU로 구성된 컴퓨팅 클러스터를 제공받아 '키미 K3' 개발에 활용한 것으로 알려졌다. 블룸버그는 31일(현지시간) 복수의 관계자를 인용, 문샷이 알리바바와 컴퓨팅 파워 공급 계약을 맺고 2만개 규모의 엔비디아 GPU 클러스터를 사용하고 있다고 보도했다.이 인프라는 문샷이 키미 시리즈를 개발하는 데 사용하는 전체 연산 능력 가운데 상당 부분을 차지하는 것으로 전해졌다.이번 사실은 미국의 반도체 수출 규제에도 불구하고 중국 AI 기업들이 여전히 엔비디아의 고성능 GPU를 기반으로