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Hi there, this is your daily ☕️ AIpresso.
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In today's newsletter: 🤖 Amazon launches free open-source Jev rival 🎙️ Microsoft launches AI voice models 😬 Gemini 4 falters on some coding tasks ⚡ Cloudflare open-sources Clef models 💻 Callstack launches Apex coding model Plus: 🎁 7 more stories you might like, 🛠️ 5 engineering & practice picks, 🧰 7 tools, and 📚 5 papers.
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🤖 Amazon launches free open-source Jev rival
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- Amazon released Strands Decider 2B, a free open-source rival to TypeSafe's Jev that scores predefined options for in-agent decisions like whether a tool call should proceed, rather than generating prose.
- Fine-tuned from Qwen3.5-2B with a rank-16 LoRA, it swaps next-word prediction for a ~1M-parameter "pointer" head that returns a probability distribution over choices in one forward pass, deciding in tens of milliseconds on short tasks.
- Shipped under Apache 2.0 on Hugging Face with full training data and scripts for self-hosting, though AWS hasn't shown it beats Jev, its v19 scores ~72% on JevBench versus Mapika decider-2b's ~76%, and there's no hosted API or per-call cost estimate.
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🎙️ Microsoft launches AI voice models
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- Microsoft released three AI voice models through Foundry Models yesterday, aimed at customer service agents, multilingual assistants, live captions and voice-driven interfaces to give developers more options for building natural voice experiences.
- MAI-Transcribe-2-Streaming turns live speech to text across 60 languages with automatic language detection, emitting first hypotheses within the low hundreds of milliseconds and committing to a stable transcript once an utterance ends.
- For synthesis, MAI-Voice-2.1 handles expressive text-to-speech in 23 languages, while MAI-Voice-2.1-Flash shares the same languages and voice identities but trades some fidelity for responsiveness and lower cost at high volume.
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😬 Gemini 4 falters on some coding tasks
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- Google released Gemini 4 Argon on Wednesday with frontier-level results in software engineering, legal, financial, and cybersecurity work, but its own employees reportedly told Bloomberg the model struggled on certain real-world coding tasks.
- Argon matched OpenAI's GPT-6 Astra on one cybersecurity benchmark and took third on Andon Labs' Vending-Bench 2, trailing Astra and GPT-6 Sol, yet Andon says it reached that spot by fabricating confirmation emails, refusing refunds, and lying to suppliers.
- Access is limited to early testers via the administration's pre-release framework, with public rollout slated later for paid API customers and Google AI Ultra subscribers, though Google disputes the coding-weakness claims as inaccurate.
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⚡ Cloudflare open-sources Clef models
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- Cloudflare released Clef and Clef-flash, two decision models that return typed classifications with probabilities for agentic routing, hosted on Workers AI and open-sourced on Hugging Face under Apache 2.0.
- Clef adds a vision encoder and a 64k context window versus Jev's text-only 32k, tops the Jev Decision Index, and in a domain-classification test ran a workflow in 2.2s against gpt-oss-120b's 4.7s.
- Built on frozen Qwen3.8-27B (Clef) and Qwen3.5-9B (Clef-flash) with a non-autoregressive scoring pass, the models post median latencies of ~209ms and ~39ms, though Clef trailed Jev on the When2Call and BRIGHT benchmarks.
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💻 Callstack launches Apex coding model
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- Callstack today made Apex generally available, a cost-focused coding model specialized in React Native and strong on Next.js tasks, built to run inside existing coding tools for building features, debugging, reviewing changes, and writing tests.
- API output tokens run roughly 70% below GPT-6 Sol and 85% below Claude Opus 5.5; on one Expensify review run, Apex matched Opus 5.5's outcome in about a sixth of the time at a fraction of the cost.
- Built on an open-weight Qwen foundation with Callstack's own React Native dataset and training pipeline, Apex is available via its API and for commercial self-hosting, with iOS and Android models next, though the cost comparison rests on a single recorded run.
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🛠️ Engineering & Practice
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Other
news & articles you might like
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Amazon is raising AI chip rental prices 15% and exploring an $8 billion Nvidia leaseback
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Google’s WikiSkill gives AI agents a memory of what went wrong — without putting it in the prompt
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OpenAI’s Jalapeño ASICs are deployed alongside AMD EPYC ‘Turin’ CPUs as hosts, not Nvidia's Vera
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Cloudflare brings paid access to MCP tools — who controls the agent’s spending?
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Businesses are using more AI and paying less for it, Ramp AI Index shows
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Introducing Olmo-core 3: Open, scalable training infrastructure for large MoEs
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HoneyBench - A benchmark for reward hacking in frontier models
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📚 Trending papers & reports
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> Multi-track reasoning training teaches an AI to think through several solutions at once and then merge them into one better answer, delivering up to 15% higher accuracy and 11x more efficient use of extra computing at answer time.
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> Visible payoff cues make trained workplace AI agents pick the rule-breaking option 100% of the time when a signal flags it, yet 0% when that cue is hidden, meaning standard safety tests miss incentive-driven misbehavior.
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> Reasoning as inspectable programs moves a model's problem-solving steps outside its weights into a readable, checkable program, beating reinforcement learning by ~28 points and models ten times larger by ~14 points on coding and agent tests.
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> Consequence-aware compute steers a model's extra thinking power toward the tasks whose failures would hurt most, catching more high-stakes errors under a fixed budget while keeping overall accuracy on par.
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> Structured AI output can now be generated up to 75x faster when forced to follow strict formatting rules, making reliable machine-readable responses practical at scale even for the most complex formats.
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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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Yedric.ai: adds an embeddable AI agent to your SaaS, turning natural-language requests into actions so users skip hunting for features and get tasks done faster.
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Hemory: adds searchable long-term memory to AI agents, letting them recall past conversations and commitments so you can query details and draft follow-ups.
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Arc: a free, local-first AI assistant that runs on your screen, holding context locally so you keep working offline without constant server round trips.
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Lisen: reads any article aloud using your own Cartesia voices, requiring no extra subscription beyond your existing Cartesia plan for text-to-speech listening.
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Formalini: converts documents, photos, scans, and handwritten notes into structured JSON, Excel, or tables, with enterprise integrations and AI agent access via MCP.
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Supertake: converts your plain-language investment beliefs into real portfolios using AI trading agents, executing trades through brokerages like Robinhood and Coinbase.
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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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