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Hi there, this is your daily ☕️ AIpresso.
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In today's newsletter: 💻 OpenAI's new model migrates old COBOL code ⚡ New diffusion AI beats GPT-6's fast model 🔬 Gemini AI finds new fusion field solutions 🎬 Kling AI 4.0 adds stereo audio ☁️ Cloudflare's Clairic reads docs cheaply Plus: 🎁 7 more stories you might like, 🛠️ 5 engineering & practice picks, 🧰 7 tools, and 📚 5 papers.
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💻 OpenAI's new model migrates old COBOL code
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- OpenAI says its latest models are now substantially better at reading and migrating legacy code in languages like COBOL, Fortran and Visual Basic, targeting the roughly 40% of banks still running COBOL systems facing a shrinking pool of programmers.
- On OpenAI's new MultiLangBench suite, which tasks models with building SQLite-compatible database engines across 20 languages, GPT-6 Astra scored an 87.4% average pass rate, more than triple GPT-5.6 Sol's, though COBOL stayed hardest.
- On COBOL-to-C++/Java/Kotlin/Python/Rust migrations that must preserve rounding and missing-record behavior, GPT-6.1 Sol hit 97.9% accuracy at high reasoning effort, dropping to 86.6% at low effort, though OpenAI warns production use still needs expert review against business rules.
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⚡ New diffusion AI beats GPT-6's fast model
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- Inception released Mercury 3, a diffusion language model it says beats speed-optimized rivals GPT-6 Luna and Claude Haiku 5.5 on quality benchmarks while running four to eight times faster.
- The model generates over 1,000 tokens per second on standard NVIDIA GPUs and supports a 1M-token context, while being over 40x larger than Mercury 1 yet holding the same speed.
- It's available via the Inception API, Baseten, OpenRouter, and Vercel, and is OpenAI API-compatible so developers can swap it in without major code changes.
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🔬 Gemini AI finds new fusion field solutions
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- Google's Gemini 4 Argon helped researchers extend exact mathematical solutions for the magnetic fields that confine fusion plasma, building on September 2026 work that overturned a conjecture physicist Harold Grad proposed back in 1967.
- Running through Google's Antigravity environment, multiple agents explored Matt Landreman's earlier results and, within hours, found ways to break a geometric symmetry restriction, yielding new configurations researchers could verify as reference cases for magnetic-equilibrium software.
- The work gives teams additional exact solutions to test plasma-modeling code against, not a path to commercial fusion, and the AI candidates still required manual derivation checks to confirm they were mathematically consistent.
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🎬 Kling AI 4.0 adds stereo audio
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- Kling AI 4.0 is out, adding two-channel stereo audio and improved lip sync across more than ten languages including German, English, Japanese, Korean, and Spanish, alongside higher resolution and more precise controls.
- The model generates up to 30 seconds in a single pass, accepts up to ten keyframe images, and comprehends prompts up to 8,000 tokens, with its "Omni Reference" system taking up to 15 reference assets per generation.
- Kling 4.0 now works through MCP and CLI for agent-driven automated workflows, and a lower-cost Flash variant tops out at 720p and 20 seconds, though API pricing and support are still listed as "coming soon."
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☁️ Cloudflare's Clairic reads docs cheaply
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- Cloudflare has released Clairic, a model for pulling structured data from documents that it says runs faster and cheaper than LLMs, now in preview on Workers AI with open weights to follow.
- Developers name the fields they want and Clairic returns matching values with a confidence score in a single pass rather than generating token by token, returning "null" for missing values and handling simple calculations and forecasts.
- In a sample complaint-handling chain with decision model Clef and GLM-5.3, the pipeline runs in 19.3 seconds versus 61.7 for GLM-5.3 alone at nearly double the cost, though Clairic can't yet reliably handle longer calculation chains.
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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.
🛠️ Engineering & Practice
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> MPS in PyTorch 2.15: PyTorch 2.15 speeds up local AI models on Apple Macs, adding memory-saving formats and faster decoding so Macs become a practical place to run and train models.
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Other
news & articles you might like
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Nscale and Anthropic are reportedly in talks for yet another $45 billion compute deal
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China’s tech giants drop $35 billion on export-compliant Nvidia RTX Pro 5500 workstation GPU orders to power AI ambitions, according to reports
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DeepSeek made its name with cheap AI models. Now it’s undercutting search API rivals, too.
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Microsoft adds tools to limit and monitor AI agents in Microsoft 365 E7
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fal launches FLUX 3 Max, claiming 22× faster AI video generation than Seedance 2.5
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Anthropic has nearly stopped hiring junior staff because AI agents now work six times as many hours
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Replit is no longer working with Anthropic, CEO says
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📚 Trending papers & reports
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> Robot vision planning lets robots figure out where to look and mentally picture what they'll see before grabbing objects, hitting ~94% success in real trials even when clutter or odd angles hide things.
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> Safety guardrails for learning agents let reinforcement learning systems enforce complex rules during training without ever violating them, with mathematical proof the agent stays safe and still learns efficiently.
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> Recommendation sets give high-stakes decision makers a spread of diverse, well-reasoned options each paired with a plain-language rationale instead of one ranked list, scoring highest on quality across deal-making, customer targeting, and content picks.
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> Mislabeled training data leaves a detectable geometric fingerprint inside a neural network's internal representations, giving teams a way to spot corrupted labels that standard training accuracy completely misses.
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> Explainable reinforcement learning gets a clear map, organizing over 250 studies by what each method explains and how, helping teams pick tools to understand why trained decision-making agents act as they do.
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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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Backdrop: build specialized AI coworkers connected to Slack, Asana, Hubspot and 1,500+ tools, with shared context and audit trails for research and reporting.
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face2social: upload a photo to find matching faces across Instagram, Facebook, LinkedIn, TikTok, and X, helping you verify identities and spot scammers.
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Train with Pasta: generates custom workouts from voice or text requests, swaps exercises on command, converts YouTube links into sessions, and keeps health data on-device.
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Dreamscale Labs: runs frontier robotics AI models like VLAs and WAMs through low-latency cloud inference, letting teams skip costly GPUs for on-demand access.
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SaidTrace: records audio on iPhone, Watch, or recorder, transcribes it on your Mac with your chosen AI, and links answers back to the source.
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Stage: publish prototypes from Figma, Claude, Cursor or GitHub, collect pinned feedback, auto-fix with Claude, and file Linear or Jira tickets on sign-off.
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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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