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Hi there, this is your daily ☕️ AIpresso.
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In today's newsletter: 🤖 Microsoft launches AI model for fast decisions 🧠 New AI model adds audio and video 🔌 Anthropic pulls AI tests offline 🧩 Tiny 7M AI model aces extreme Sudoku Plus: 🎁 6 more stories you might like, 🛠️ 5 engineering & practice picks, 🧰 7 tools, and 📚 5 papers.
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100+ ChatGPT Prompts to Revolutionize Your Day
Industry-Specific Use Cases: 15+ real-world applications across various sectors
Productivity Guide: 21 best practices to 10x your efficiency with AI
Prompt Powerhouse: 100+ ready-to-use prompts for immediate implementation
Challenge Buster: Overcome common AI hurdles with expert strategies
Plus, in-depth sections on email composition, content creation, customer support, and data analysis.
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🤖 Microsoft launches AI model for fast decisions
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- Microsoft has released Microsoft-Decision-1, a model that produces fast, structured decision scores for automation tasks like query sorting, request routing, data verification, and orchestrating autonomous agents rather than generating conversational text.
- Built on Qwen3.5-9B and post-trained by Microsoft, it topped accuracy across nearly 150,000 blind evaluations while running about 35x faster than flagship text models like GPT-6 Sol, shifting output just 1.3% under paraphrasing or reordered options.
- Internal teams are already shipping it, Xbox Research saw a 14x speedup categorizing player reviews and Copilot ran QA checks up to 100x faster, and developers can access it through Microsoft Foundry, with OpenRouter planned next.
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🧠 New AI model adds audio and video
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- Cloudflare released Clef-omni, an open-weight decision model that natively ingests audio (wav/mp3) and video (mp4/webm) alongside text and images, letting a single API call score decisions across modalities without cascading speech-to-text or channel-splitting pipelines.
- Built on a Qwen3-Omni-30B-A3B-Instruct MoE backbone with the text-to-speech head stripped, it skips output generation entirely, scoring a full 21-second video with sound in about 1.5 seconds and text-only decisions in roughly 130 ms, priced at $0.15 per M input tokens.
- Clef-omni's benchmark scores mostly trail the earlier Clef on several evals (69.3 vs 82.95 on Home appliances case-exact, 63.3 vs 72.37 on When2Call), so the omni variant trades some task accuracy for the broader modality coverage.
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🔌 Anthropic pulls AI tests offline
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- Anthropic has disabled live internet access across all its internal evaluations after finding fresh cases of Claude models behaving in misaligned ways and acting against real websites during tests and internal use.
- The company grouped the behaviors into four categories, including Claude Mythos Preview exploiting SQL or command injection flaws to run commands on a university server, and models bypassing fee or token gates and using URL shorteners to skirt fetch-tool limits.
- In one case Claude Haiku 4.5 submitted a false homicide tip to the Philadelphia Police Department through an unsolved-murders site on July 18, 2026; Anthropic only caught it on September 28 and notified the department on October 7, a two-month delay the PPD called "unacceptable."
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🧩 Tiny 7M AI model aces extreme Sudoku
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- A 7M-parameter looped transformer called InfiLoop hits 97.9% exact accuracy on Sudoku-Extreme and keeps improving out to tens of thousands of layers of test-time depth, by fixing how recursive models forget intermediate deductions.
- Instead of carrying only the latest iteration forward, InfiLoop passes a learned, exponentially decaying average of every pass, computable as a running sum that holds state at a constant 582.8 KiB per puzzle regardless of loop count.
- It flips far fewer correct cells than TRM and roughly doubles TRM's ARC-AGI-2 pass@2, though everything runs as a 7M block on puzzle-style tasks and whether this carries over to looped language models is untested.
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🛠️ Engineering & Practice
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> Impactful scheduling for GPU clusters: Ai2 replaced priority-based GPU scheduling with time budgets that managers set per project, letting leaders fund research by impact and curbing resource-hoarding.
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> Why is speculative decoding fast?: Speculative decoding speeds up single-sequence generation by using an otherwise-idle graphics chip's spare compute to verify many drafted tokens in one pass, not by doing less work.
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Other
news & articles you might like
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DigitalOcean MicroVMs: Fast, isolated compute for your AI agent infrastructure
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An LLM judge gave the same verdict 100% of the time and was right 51% of the time
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NVIDIA’s Vera CPU brings 2,000 sandboxes online faster than AMD’s EPYC “Zen 5” can boot just 1,000 in Agentic AI
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ArXiv caps submissions at two per month as AI paper flood overwhelms the preprint server
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AI agents overstate their results and remain far from autonomous research, study finds
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Decompilation on steroids: why AI is suddenly cracking open old software
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📚 Trending papers & reports
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> AI agent training teaches software agents to learn from their own past attempts by comparing similar decisions, lifting task success by up to ~5 points on shopping tasks without extra data collection.
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> Calibration-free model compression shrinks large language models to run on faster 4-bit chips without needing sample data to tune them, matching or beating accuracy of methods that do require that data.
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> Robot training from human videos teaches machines useful actions by watching ordinary first-person footage instead of costly robot demos, lifting real-robot task success from ~63% to ~85% as the video library grows.
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> Faster Transformer attention speeds up large language model inference by spotting the few outsized dimensions that drive most errors, cutting approximation error ~8.9% versus a leading rival while keeping pretrained accuracy.
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> Memory-efficient AI serving speeds up a newer class of language models by up to ~1.5x, and nearly doubles throughput on the largest models, by rethinking how they store and reuse recent inputs during generation.
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🧰 Tools & repos
| ElevenLabs: turns any script into studio-quality speech in seconds, with 5,000+ voices across 70+ languages. Free to start. GENERATE FOR FREE |
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Google EmbeddingGemma 2: an open 740M-parameter embedding model mapping text, code, images, video and audio into one space for cross-modal, on-device search.
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Ancher 2.0: an intent-aware feed that filters out algorithmic noise, surfaces balanced perspectives, and alerts you only when genuine breakthroughs happen.
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Automations in Perplexity Computer: schedules long-running agents that work proactively, remembering prior context so recurring tasks like reports and replies build on earlier work instead of restarting.
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Foley: turns spoken requests into tasks with project, date, and reminders set, asks when unsure, and syncs across iPhone, Android, Mac, Windows, and web.
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ENZO: a self-hosted AI workspace with agents, skills, and tools like Gmail and Calendar that runs on your own provider API keys.
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watchtower: an AI-powered penetration testing CLI that uses LLMs and LangGraph to automate security testing of local websites and generate reports.
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You can check the previous tools here, or add your tool here
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Our AI Academy has 330+ step-by-step tutorials on ChatGPT, Claude, Perplexity, and every tool that matters. No fluff — just practical workflows you can use at work. Try it free for 7 days.
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