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LLMs are accelerating software delivery and quietly expanding your attack surface. ⚠️ Prompt injection. ⚠️ Sensitive data leakage. ⚠️ Jailbreaks. ⚠️ Autonomous agent failures. ⚠️ Security risks traditional models don’t cover. If your team is building with AI, “we’ll secure it later” is no longer an option. At AI Native DevCon, Joseph Katsioloudes (GitHub Security Lab) will teach how to secure AI systems in the real world, through hands-on, challenge-based exercises designed for engineers. What you’ll learn: • Real LLM attack techniques developers must recognize • Practical defenses to harden AI systems in production • How to think like a threat actor when building with AI • Patterns + guardrails for AI-native security • Live exercises from real security incidents 🎟 Secure your seat at AI Native DevCon Fall | Nov 18–19 Use code JOSEPHK25 for 25% off your all-access pass.

AI agents feel magical, until they touch real production code. Legacy systems, complex abstractions, weird dependencies, and years of accumulated context break today’s “10x” AI coders fast. The result? Hallucinated APIs, broken refactors, and trust evaporating in minutes. At AI Native DevCon Fall, Ray Myers (Chief Architect, OpenHands) explains why LLMs fail on mature codebases, and how to fix it. What you'll learn: • Why LLMs struggle with legacy + evolving systems • Techniques to make coding agents reliable in production • How benchmarks like SWE-bench track real progress • Patterns for guiding AI inside large codebases • Emerging workflows for AI-assisted maintainability This isn’t about hype or toy repos, it’s for teams shipping software that actually matters. 🎟 Get your ticket to AI Native DevCon Fall Use code RAYM25 for 25% off your all-access pass.

Benchmarks say which LLM is “best at coding.” Real engineering says otherwise. Models that ace leaderboards can still fall apart in real codebases — messy repos, multi-file context, shifting requirements, debugging loops, production pressure. At AI Native DevCon Fall, Adam Larson (Founder & CTO, Raleon) will share how to evaluate coding models where it matters, inside real developer workflows, not synthetic tests. You’ll learn: • What actually defines LLM coding ability • Why benchmark scores mislead • How to test models in real engineering environments • Practical systems to stress-test AI coding assistants If you're serious about shipping AI-augmented software, not demos, this session is for you. Use code ADAM25 for 25% off your all access pass.

Software Factories are extremely valuable, but hard to build. Tessl’s new product may have just solved that 🙂 - Simple onboarding turns a repo into a factory in moments. - Template loops for code review, vulnerability remediation, cost optimization and more. - Built-in self-improvement for loops, compounding your factory’s value. - Interactive factory floor for tracking activity and solving problems. - Clear and simple metrics to know what you’re getting. Tessl factories are fully built on open skills, which you create, hone and own indefinitely. We help you create skills from your history, and the factory starts to self-improve them from here. Lastly, Tessl factories are declarative and easy to understand and manage. Config files composing skills and environments into actions, actions and triggers into loops, and multiple loops into a factory. This is the equivalent of Infrastructure-as-Code for factories. Instead of a bunch of disparate scripts you piece together to understand what’s happening, they codify what the factory does. This means Tessl factories aren’t just easy to setup, they’re easy to manage too. At the end of the day, our job as tool builders is to delight our users. Solve real problems with strong technology and great UX, that help them run fast. I feel the Tessl Factory is getting this job done. If it wasn’t obvious, I’m excited by our new product 🙂 At the end of the day, our job is delight our users, and I firmly feel the Tessl Factory does so. As part of the launch, we’re offering $200 free credits to all users. Try it out, and let us know what you think - I can’t wait to see what you build. https://lnkd.in/edXC7JYB

AI agents can write code faster than your team can review it. That creates a new engineering bottleneck: more PRs, more review overhead, and more opportunities for issues to slip through. In this on-demand session, see how Tessl approaches code review for AI-native development, including how to: ✓ Review agent-written code against your own engineering standards ✓ Review the full PR, not just an isolated diff ✓ Carry context across fixes and re-reviews ✓ Build code review into increasingly autonomous engineering workflows Fill in the form to watch the session on demand.

Your developers are using AI coding agents. But can your automated review process actually keep up? AI can dramatically increase the volume of code a team produces. The challenge is that automated reviewers can still miss important issues, generate noisy feedback, or lack the context needed to understand what your team considers good code. That’s where Tessl brings value. In this on-demand session, explore how AI-native code review can help engineering teams: ✓ Catch issues automated reviewers may miss ✓ Review code against your own engineering standards ✓ Apply deeper context across the PR and codebase ✓ Reduce low-value review noise ✓ Build stronger feedback loops around agent-generated code As AI writes more of the code, the quality of the review layer becomes increasingly important. Watch now on demand.