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Hi there, this is your daily ☕️ DevShot.
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In today's DevShot:
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🧱 Amazon runs PyTorch on its own AI chips 🐋 MiniMax M3.1 Flash cuts agent costs 70% 💾 OpenAI's GPT-6.1 Sol nails COBOL migration ☕ Java finalizes structured concurrency 🔍 New Clairic models extract data faster Plus: 🎁 7 more stories you might like, 🛠️ 5 tutorials, 🧰 7 tools, and 📚 5 papers.
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🧱 Amazon runs PyTorch on its own AI chips
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- Amazon today released TorchNeuron, an open-source toolkit that lets developers run PyTorch workloads on AWS Trainium chips without extensive code changes, just a setting that tells each workload which accelerator to use.
- It supports Trainium2 and Trainium3, ships pre-packaged NKI operators for tasks like building mixture-of-experts models, cutting memory use, and speeding quantization, and integrates with tools such as the TorchTitan training engine for sharded training runs.
- Cartesia AI and the Lieber Institute for Brain Development are already using it for inference and medical-research workflows, and Amazon open-sources the toolkit today at the two-day PyTorch Conference in San Jose.
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🐋 MiniMax M3.1 Flash cuts agent costs 70%
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- MiniMax released M3.1 Flash, an update to its M3 model that runs agentic workloads 70% cheaper than its predecessor while serving inference nearly three times as fast, topping 200 tokens per second at peak.
- The 428-billion-parameter model gains its speed from more aggressive inference-time sparsity, keeps reasoning on for every request across five effort levels from Low to Max, accepts text, image and video, and handles prompts up to 1 million tokens.
- It costs a flat 15 cents per million input tokens, matching DeepSeek's V4.1-Flash off-peak rate; MiniMax will publish the Flash weights later and ship a more capable Pro model, with M3.1 Flash available now via its API and MiniMax Code app.
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💾 OpenAI's GPT-6.1 Sol nails COBOL migration
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- OpenAI's GPT-6.1 Sol scored 86.6% on the company's own COBOL migration benchmark at low reasoning effort, up from just 5.4% for GPT-5.6 Sol, and nearly saturated the test when cranked to higher effort.
- The test converts COBOL into C++, Java, Kotlin, Python or Rust and only counts a migration if every hidden test matches the original byte for byte, catching bugs like fixed-point arithmetic that silently rounds and alters financial results.
- OpenAI ran both evaluations itself with no outside verification and warns production use "still requires expert review and testing against the system's business rules and dependencies"; it is publishing the related MultiLangBench setup, tests and per-language results on GitHub.
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☕ Java finalizes structured concurrency
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- Java's structured concurrency API has reached final status in JDK 28 via JEP 543, closing Project Loom's longest preview cycle after seven rounds with no API changes since JDK 27's version.
- The API treats related subtasks in separate threads as one unit, so a failure cancels siblings and the parent scope can't exit before children terminate, centered on a sealed scope opened by factory methods that start virtual threads by default.
- For teams on JDK 27 the move is a build change, not a code change, drop the preview flags and recompile, while maintainers can finally ship structured-concurrency features without forcing preview flags on users; general availability is set for March 2027.
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🔍 New Clairic models extract data faster
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- Cloudflare previewed Clairic, an "extraction model" hosted on Workers AI as
@cf/cloudflare/clairic that extracts, computes, estimates, and forecasts typed values from unstructured text and images faster and cheaper than an LLM, with open weights promised soon.
- A request sends state plus a map of typed fields, and Clairic answers each in one parallel forward pass with no output tokens, returning numbers, dates, lists, or spans with character offsets, confidence scores, and a p_null, so asking thirty fields costs about the same as three.
- Because it generates no tokens it can't emit invalid JSON or hallucinate text, returning either a valid value of the declared type or null; computed fields like a summed total or days-between-dates come back worked out, and estimates ship with a median and 80% interval.
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100+ ChatGPT Prompts to Revolutionize Your DayIndustry-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.
🔗 Other News & Articles
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OpenBSD 8.0 released with Intel Arc A-Series, other hardware improvements
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Apple adds mystery folder to your PATH and everyone is apparently just okay with that
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Windows 98 SE users get a Thunderbolt 4 driver, thanks to AI assistance
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One sentence changed Claude Code’s overreach rate from 0% to 17.1%
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You can now modify the vBIOS on Nvidia gaming GPUs without flashing thanks to a solo developer’s project
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Introducing expo-app-intents: Siri, Shortcuts, Spotlight, and Apple Intelligence for Expo apps
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Vercel’s vgpu: A WebGPU library built for AI agents
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📄 Papers & Resources
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> AI testing toolkit pulls accuracy, speed, tool use, and retrieval quality into one standardized scorecard, so teams comparing models and automated agents can spot hidden failures instead of trusting mismatched numbers.
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> Clarifying questions let an AI agent ask a developer a few targeted questions before writing formal software specs, lifting accuracy up to ~66%, a ~6-point gain, but only when a different AI model does the writing.
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> AI agent tool reliability gets a bolt-on fix that catches and repairs errors when tool menus or instructions shift after launch, lifting task accuracy on one benchmark from ~58% to ~81% without retraining the model.
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> Coding benchmark hints often leak answers that let AI shortcut the hard steps a test was meant to measure, so this work offers minimal-hint techniques that keep coding tests honest and useful for longer.
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> Assembly clone search can now spot reused, vulnerable code inside software built for computer chip types and libraries it was never trained on, closing a blind spot that let security flaws silently spread through released software.
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🧰 Latest Tools
| 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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tiun: an AI-native backend bundling authentication, payments, customer data, and analytics into one platform, so teams skip stitching separate services together.
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Jev: adds structured AI-driven decision-making to automation workflows, letting developers build reliable branching logic without hand-coding every rule
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Switch: open-source workspace connecting people and AI agents across Slack, Teams, Discord, and Mattermost, preserving context during handoffs without vendor lock-in.
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Monospace from Directus: wraps any SQL database with instant REST and GraphQL APIs plus a no-code app for collaborative data management.
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iFixAi: audits AI agents with 250 inspections across 69 misalignment categories, flagging failures and giving engineers evidence to investigate and fix issues
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Onepin: a QA layer for AI voiceover that fixes mispronounced names, prices, and dates across 30+ voice engines without re-rendering takes.
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