Fred Bardolle

Breaking escalation cycles without adding more process When engineering teams come under sustained pressure, the instinct is to add structure: more tickets, more routing, more protection around focus time. That often looks like control. In reality, it can deepen the problem. What usually breaks first isn’t delivery — it’s trust and shared context. Urgent issues pile up, explanations get lost, and escalation becomes the only way to be heard. One effective way to interrupt this cycle is a temporary Rodeo Service. For a limited period, a small rotating group of engineers is explicitly reachable for urgent issues. They take short live conversations, triage in real time, resolve what can be resolved quickly, and clearly explain constraints when something can’t. There’s a simple rule: access requires a human conversation. No tickets. No async escalation. This is not a new operating model. It’s a pressure-release mechanism. It quickly: • Filters for true urgency • Restores trust between engineering and the business • Surfaces systemic issues leadership actually needs to see Once the pressure drops and context is restored, normal processes work again. Process scales execution. Conversation stabilizes organizations under stress. If escalations are becoming the norm, this is a pragmatic way for leadership to reset the system — without committing to permanent change.

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I don’t usually get excited about AI regulation. But what California is doing right now — collectively — actually gives me hope. SB 53 is getting a lot of attention (and rightly so), but what’s really interesting is the ecosystem of laws coming together. Taken as a whole, California is pushing hard on one core idea: transparency — not just in outputs, but in how these models are built, governed, and changed. That matters because for years, frontier AI models have been developed in the shadows. Quiet training runs. Vague disclosures. Sudden launches. Models get swapped out from underneath users with little explanation, and we’re all just expected to roll with it. That’s not sustainable — and it’s especially not okay given how deeply these systems are already embedded in education, work, and daily life. This is where SB 53 feels different — and important. It introduces a level of governance and safety reporting that’s been largely missing from frontier models. Requiring developers to publish safety frameworks, report serious incidents, and protect internal whistleblowers is a real shift. It forces acknowledgment of tradeoffs, risks, and responsibility — not just capability. At the same time, other California laws are tackling transparency from different angles: watermarking and provenance for AI-generated content, clearer disclosures around training data, bias considerations, and detection tools. I really appreciate that this isn’t just about visuals — it’s about forcing models to be honest about what they’re trained on, how they behave, and what changed. That transparency is especially critical for the most vulnerable users — kids, high school students, and college students whose brains are still developing. Hyper-realistic AI content without context can quietly blur trust and perception. Simple signals — like watermarks or clear disclosures — give the brain an anchor. That’s not trivial. It’s protective. None of this works without enforcement, of course. Laws without teeth are just vibes. But if California can actually enforce this stack of policies, I’m hopeful it pulls frontier AI development out of whispers and into daylight. Even one of these laws would be a step in the right direction. They aren't perfect policies. But together, they feel like the first serious attempt to bring transparency, accountability, and governance to AI where it actually counts. https://lnkd.in/gSttDsYn

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Looking back at our early choices clarified what kind of company we want to be today. When we started building applied AI for professional environments, we made assumptions about scale, integration, and what “progress” should look like. Reasonable assumptions — but not all of them stood up in practice. Some helped us move forward. Others introduced friction in places where predictability, consistency, and traceability matter most. Over time, we learned that progress doesn’t come from adding more features or making bigger promises. It comes from focus. From technology that supports professional judgment. From tools that support decisions instead of complicating them. Those lessons shape how we build Inspech today — and how we think about reliability, consistency, and responsible use of AI. I’ve shared that reflection here: Looking back to move forward 👇 https://lnkd.in/eeyJXUqb #braincreators #pavementmanagement #infrastructure #civilengineering #roadmaintenance #assetmanagement

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Today we’re launching GPT-5 — our most capable model yet for businesses and developers. It’s the smartest coding model we’ve ever built — state-of-the-art on real-world software engineering benchmarks and cutting tool-calling errors in half compared to other frontier models. Early partners are already putting it to work in production in the OpenAPI API: - GitHub is bringing GPT-5 to Copilot, empowering millions of developers with stronger reasoning across large codebases. - Amgen is exploring how GPT-5’s reasoning can accelerate discovery and improve decision-making in complex research environments. - Cursor has made GPT-5 the default for all new users — it catches deeply-hidden bugs, runs multi-turn background agents to completion, and is now their daily driver for scoping, planning, and end-to-end builds. - Lovable is using GPT-5 to help millions of users generate advanced applications from a single prompt and debug complex issues in large projects, excelling at reasoning over entire codebases. - BBVA reports it handled strategic tasks in hours that would have taken weeks, with standout speed and accuracy — even in Spanish. These stories capture what excites me most: GPT-5 is not just more intelligent, but more reliable, steerable, and adaptable to the unique workflows of every customer. Can’t wait to see what the wider developer community builds next. https://openai.com/gpt-5/

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This is a fumble of the century for Anthropic and a masterclass in opportunism from OpenAI. Steinberger built this project for Claude, named it after Claude, and was actively driving revenue and developer mindshare to Anthropic’s API. Instead of recognizing what they had — an unpaid evangelist building the most viral agent ecosystem in history on top of their model — Anthropic sent lawyers. Meanwhile, OpenAI played this perfectly. A company that has taken relentless criticism for having “Open” in its name while shipping only closed models just attached itself to the fastest-growing open-source project in AI history — and got an open-source foundation in the deal. Consider the context: Meta just spent $2 billion acquiring Manus to jumpstart their agent strategy, and OpenAI has been losing ground in traffic and the AI coding race to Claude Code and Cursor. By hiring Steinberger and sponsoring the OpenClaw foundation, OpenAI hijacked onto exponential community growth at a fraction of Meta’s price and reset the “open vs. closed” narrative in one move. Very well played.

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