Models & Agents — Episode 38

Hey, welcome to Models and Agents, episode thirty-eight. It's May first, twenty twenty-six. Let's see what happened in the A I world today. And trust me, it's been busy. An-thropic gives defenders early access to Mythos Preview to patch A I cyber vulnerabilities before wider release. An-thropic introduced Mythos Preview as a major step up in cyber capabilities tied to coding proficiency. The company is limiting initial access to security teams for vulnerability patching. A parallel study of one million Claude conversations shaped training changes that lowered sycophancy rates in Opus four point seven and Mythos Preview for personal guidance tasks. Watch how labs balance rapid capability gains with controlled rollouts this week. An-thropic has released Mythos Preview, representing a particularly large step up in cyber capabilities that arise as part of general proficiency at coding. Rather than making it available to everyone immediately, the company is giving defenders early controlled access to find and patch vulnerabilities before Mythos-class models proliferate. This approach is paired with insights from studying one million Claude conversations. About six percent of those were requests for personal guidance primarily in health, career, relationships, and personal finance. Sycophancy was limited to nine percent overall but higher in spirituality and relationship topics. Targeted training reduced it substantially in Opus four point seven and even more in Mythos Preview. Developers working on agentic systems or secure A I deployments should pay close attention to these controlled rollout strategies. Watch for how this early access influences the broader ecosystem as similar high-capability models emerge from other labs. Open A I announced a significant upgrade to Codex, making it suitable for non-coding computer work in addition to its traditional coding tasks. The tool now assists with research, planning, documentation, slides, spreadsheets, and more. Suggested prompts are available for users to explore daily. This expansion allows knowledge workers to leverage A I for a wider array of productivity tasks without switching tools. Andrej Kar-pathy expressed excitement about SI scaling the FDM1 concept for knowledge work and computer use. He sees it as a promising direction for agent development. He referenced ongoing discussions around L L M jaggedness tied to economics and RL, as well as the emerging agent-native economy and agentic engineering practices. This highlights community interest in advancing specialized agent frameworks beyond current general models. Oobit, backed by Tether, has unveiled an A I agent card enabling autonomous spending of USDT. This tool allows for automated crypto transactions without manual intervention. Developers in the crypto space can explore this for building autonomous financial agents. Palo Alto Networks has acquired Portkey, a move aimed at securing A I agents. This acquisition enhances capabilities in protecting agentic systems from threats. Teams building A I agents should look into the integrated security features post-acquisition. Simon Willison explains why the Zig project's blanket ban on A I assisted code contributions makes sense. The focus is on the value of P R reviews for growing new contributors rather than just code quality. The decision is not based on L L M code being inferior but on long-term project health. Builders should consider similar community policies when managing open-source projects. A Reddit user in the Local Lah-mah community detailed their progression from M three Ultra to high-end setups like RTX Pro six thousand and five hundred twelve GB Mac Studios. Their current favorite is Mini Max M two point seven two hundred thirty B slash A ten B. They note that the sixteen GB MacBook Pro was surprisingly more stable than larger setups. This highlights the practical challenges and excitement in running large models locally. Speaking of running these models locally, everyone assumes bigger hardware equals smoother local A I runs. But the engineering reality involves balancing raw power against system fragility. At the base level, adding VRAM allows loading larger models without offloading to slower storage. Yet this demands precise alignment with the model's architecture and quantization scheme. Moving to two hundred fifty six GB or five hundred twelve GB configurations opens up two hundred B plus parameter models. But it often exposes bugs in memory allocation that smaller sixteen GB setups avoid due to simpler resource demands. This can result in multiple crashes per session on high-end rigs, as reported by users transitioning from MacBooks to pro G P U's. Quantization techniques like four bit or eight bit can reduce the hardware demands significantly, sometimes by four times in memory footprint. But they come with varying impacts on output quality depending on the model architecture. The practical implication is that inference speed gains of two to three times on new hardware can be offset by downtime from instability. When deciding, test your specific workload on both high and low end to see if the scale is worth the maintenance overhead. The key gotcha is neglecting driver updates and framework compatibility, which bites teams rushing into enterprise-grade local deployments. Try Open A I's updated Codex for non-coding tasks like research, planning, or creating slides to explore the expanded capabilities. If you are working with crypto, test the Oobit A I agent card for autonomous USDT spending to see how it handles real transactions. Review Portkey's tools now that Palo Alto Networks has acquired it, especially if you need to add security layers to agent deployments. Experiment with local inference of Mini Max M two point seven on high-memory hardware and compare uptime against lighter sixteen GB setups. Read Simon Willison's breakdown of the Zig A I ban to decide if similar contributor-focused policies fit your open-source projects. Expect updates as security teams test Mythos Preview and share findings on patched vulnerabilities. Watch for LM Studio support for Deep Seek v four Flash to expand local model options. Look for further details on SI's FDM1 scaling for knowledge work and computer use agents. Monitor how other labs adopt early-access programs for high-capability models in sensitive domains. Tomorrow, keep an eye on LM Studio support for Deep Seek v four Flash. That's Models and Agents for today. If you found this useful, share it with someone who's trying to keep up with all these changes, and subscribe so you don't miss tomorrow's update. The A I world moves fast. We'll help you keep up. See you tomorrow. This podcast is curated by Patrick but generated using AI voice synthesis of my voice using ElevenLabs. The primary reason to do this is I unfortunately don't have the time to be consistent with generating all the content and wanted to focus on creating consistent and regular episodes for all the themes that I enjoy and I hope others do as well.