Steren Giannini

💡 AI doesn’t just change how code is written – it changes expectations and outcomes. In the final section of Jellyfish’s AI Adoption Guide, we explore how expectations for engineering leaders are evolving as AI becomes embedded across the SDLC. Because the organizations pulling ahead are treating AI as a transformation, not a tooling upgrade. Inside, you’ll explore: - Why AI adoption is quickly becoming a leadership mandate, not an option - How to balance ambitious goals with realistic expectations - What cultural investments are required to sustain long-term gains - How data can be used to align executives and engineering teams The teams that win in the AI era won’t just have better tools – they’ll have leaders who know how to guide change. Download today: https://lnkd.in/dut_6WpQ

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Elie Schoppik has spent his career at the intersection of education and engineering. He built early ed-tech tools that helped people actually retain knowledge, founded a Bay Area coding bootcamp that trained thousands of developers through practice and feedback, and now leads Technical Education at Anthropic. Despite all that experience, Elie says most teams are using AI wrong: they’re treating it like an all-knowing oracle instead of a tutor. This is why Elie designs prompts, examples, and workflows that treat AI as a teacher, instead of a shortcut. In our conversation, we discuss: • Why relying on AI for “fast answers” erodes judgment and verification • How to turn prompts into guided lessons • Why context engineering is the most underrated skill in 2025 • Practical exercises any team can run tomorrow to raise AI fluency • What leaders should steal from Anthropic’s approach to technical education  Episode is live now — full conversation linked in the comments.

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🚨 The battle for the future of software is no longer about who can write code fastest — it’s about who owns the data and context to power AI. Generative coding agents and tools from Anysphere, GitHub Copilot, Windsurf Cognition and poolside are commoditizing software development. In this new era, context is king and data is the moat. Winners will be those who: 1️⃣ Embed deeply into workflows to capture rich, high-fidelity data 2️⃣ Engineer context precisely at inference time 3️⃣ Reinforce models through usage, not static prompts Legacy SaaS vendors face eroding moats. AI-native apps with data flywheels and contextual intelligence are rising fast. 👇 Full article in the comments. If you’re building in this space, I want to hear from you. Thank you to Jeff Haynie, Jason Warner, Çağla Kaymaz and Vedant Agrawal for providing input and feedback on this article! #AI #Software #VC #DeveloperTools #ContextIsKing #FutureOfWork

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Big week for us at Gambit Robotics, Inc. 🔹 Launched on Kickstarter and funded in 11 minutes 🚀 🔹 Early-backer pricing still available (for now) 🔹 Anthropic spotlighted Gambit as a reference case for embodied AI — showing how Claude powers real-time, multimodal cooking assistance in actual kitchens Cooking is a physical, embodied problem. Sensors are noisy, inputs are incomplete, users don't behave predictably. That messiness is exactly what Claude helps us handle: turning raw sensor data into calm, trustworthy guidance in real kitchens. Claude reasons over vision, thermal data, timing, and recipe state together — maintaining long-running context throughout an entire cooking session. This is the core challenge of embodied AI, and why reasoning through messy, real-world conditions matters. 👉 Back Gambit on Kickstarter (link in comments) 👉 Read the Anthropic feature (link in comments)

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Buried in the Claude Haiku 4.5 system card was a really interesting nugget that most people missed, but it could have a huge impact for anyone building with LLMs “For Claude Haiku 4.5, we trained the model to be explicitly context-aware, with precise information about how much of the context window has been used. This has two effects: the model learns when and how to wrap up its answer when the limit is approaching, and the model learns to continue reasoning more persistently when the limit is further away." For years, context awareness and management have been handled by developers and it's one of the hardest parts of building agents. Teams built systems to count tokens, monitor context usage, and trigger downstream functions like summarization or condensation. With Claude 4.5 Haiku, context awareness is now trained into the model itself. It reminds me of what happened with Chain of Thought. Something that started as a prompt engineering hack but eventually became part of model training (reasoning models). There’s still plenty of work to do around context management, but if this is the direction things are heading, the next major leap in model capability may already be taking shape: models that can manage their own context intelligently. S/O to Simon Willison who originally highlighted this on his blog (linked below)

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