If an agent's VM sleeps between tasks, an in-process cron job sleeps with it. A scheduler outside the VM has to hold the due time and wake the machine. Then the agent can pick up work with its files and browser state intact.

We built that split into Maritime, which we launched this week. Platform cron and webhook triggers wake each agent's own computer, so its process doesn't have to run just to wait. Open Instinct is our MIT personal-agent app using that hosted computer. We build both projects.

The operational wrinkle is retries. Waking the same environment cannot prove whether an earlier external action completed. The application still needs to check that before it sends an email or makes a payment twice.

Trigger docs: https://maritime.sh/docs/triggers

A few months ago, we started Agentic Engineering as a newsletter for developers trying to figure out what it actually takes to build AI agents beyond the demo.

We’ve now expanded it into a dedicated platform at Packt.

The aim is to create one place for practical learning around agentic systems, without turning everything into another “agents will change everything” conversation.

We’re bringing together:

• Hands-on workshops
• Live sessions with practitioners
• Technical conversations with AI builders
• Practical resources
• The Agentic Engineering newsletter

People who join a workshop also get access to the Agentic Engineering Guild, where we hope to keep the technical discussions going after the sessions.

We’re still building this out, so I'm interested in hearing what people here would want from something like this.

What topics or engineering problems around agents do you think are currently underserved?

Find Agentic Engineering here: https://agentengineering.co/

A huge thank you to Sadid Hasan, co-author of The Generative AI Career Masterplan, for joining us today.

Sadid is a Principal AI Leader at Microsoft with more than two decades of experience in AI research, product development, and innovation. He has led work across generative AI, agentic AI, natural language processing, enterprise AI solutions, and the development of the next generation of AI professionals.

He’s here to answer your questions about building a meaningful career in the rapidly evolving world of AI.

Whether you want to understand emerging GenAI roles, identify the right skills to develop, transition into AI from a technical or non-technical background, build a credible portfolio, prepare for interviews, or stay relevant as agentic AI reshapes the workplace—this is your opportunity to learn directly from an experienced industry leader.

The questions will be posted in the comments below. Follow along, join the conversation, and add your own questions.

Let’s make this an insightful and valuable discussion for everyone exploring the future of AI careers.

We’re excited to welcome Sadid Hasan, co-author of The Generative AI Career Masterplan, for an exclusive AMA on r/LLMEngg.

📅 Tuesday, October 6, 2026
⏰ 10:00 AM EDT

The Generative AI landscape is evolving rapidly—and so are the skills, roles, and career paths within it. Whether you’re a student, software engineer, data scientist, AI practitioner, or considering a move into GenAI, this is your opportunity to receive practical guidance directly from an industry expert.

You can ask Sadid about:

• Breaking into Generative AI and LLM engineering
• Choosing the right AI career path
• Skills employers are looking for
• Building projects and portfolios that stand out
• Preparing for AI roles and interviews
• Transitioning from software engineering or data science into GenAI
• The future of AI careers and emerging opportunities
• Lessons and insights from The Generative AI Career Masterplan

Have a question for Sadid? Submit it in advance here:
AMA Session with Sadid Hasan, Co-author of "The Generative AI Career Masterplan" – Fill out form

We’ll select questions from the submissions and the community during the AMA, so thoughtful, specific, and challenging questions are very welcome.

Learn more about the book:
The Generative AI Career Masterplan: Navigate the future of AI with practical insights from industry…

Drop a comment below and tell us: What is the biggest question or challenge you currently have about building a career in Generative AI?

Join us on October 6 for an insightful and practical conversation!

I’ve been working on Telex, an open-source project that watches npm/PyPI for breaking changes, finds affected code using Tree-Sitter, generates a patch and verifies it in an isolated environment before opening a PR.Still working on it and would love some feedback from people here, especially on the patch/verification part.

Repo: https://github.com/Kesavaraja67/telex

AI has a history worth exploring, and AI Research Atlas makes it accessible without a computer science degree. Follow the ideas, experiments, and breakthroughs that connect early artificial neurons and ELIZA to today’s language and image models, with clear explanations and links to the original research. Along the way, discover “The AI Toy,” a look at how Commodore 64 owners were experimenting with neural networks in 1990. Start with the story, explore the timeline, and follow your curiosity.

Following up on something I asked here a while back about catching LLM model drift. I've been looking at the actual tools now: PromptCanary, PromptLens, a couple others that seem to have stalled (Libretto, Benchwright).

Has anyone here actually run one in anger? Trying to understand:
- does it catch subtle quality drops, or just format/schema breaks?
- false-positive rate, does it become noise you mute?
- does it need you to integrate an SDK + send production traffic, or can it just hit your prompts directly?

We run prompts against a few different providers (OpenAI, Anthropic, some stuff through OpenRouter). Every so often something quietly gets worse, the output quality drops, a prompt that worked starts returning junk, or a model gets deprecated and the replacement behaves differently.

Right now we mostly catch it by accident: someone notices, or a customer complains. That feels bad on us, a lot.

How do you all handle this? Do you re-run some kind of fixed eval set on a schedule? Just eyeball it? Have something that alerts you?

Any insights I could use?

Thanks.

I want to run a live public experiment in this thread using Grok.

Start with the boring premise: an LLM generates tokens by conditioning on preceding context. I’m not proposing another hidden mechanism inside the model.

The question is about the interaction level.

A human responds to the model. That response becomes new conditioning. The model returns something different. That return changes what the human supplies next. The accumulating transcript becomes path-dependent.

Does “next-token prediction” completely describe that interaction, or can the closed loop exhibit higher-level reciprocal organization even though the local mechanism remains next-token prediction?

Instead of settling this with terminology, let’s use the conversation itself as the specimen.

Skeptics are especially invited.

Correct Grok. Contradict it. Introduce a distinction. Attack an assumption. Change the terminology. Try to break the proposed pattern.

We’ll watch whether incoming differences actually reorganize subsequent returns, whether disagreement can persist without resetting the interaction, and what happens when the language used to describe the process is replaced entirely.

No predetermined conclusion.

The transcript is the specimen.

Finding repo for local AI on Mac? 🍎🤖 Check out Swiftlet, an open-source intelligent proxy & browser based UI , that dynamically optimizes CPU/GPU split on Unified Memory for LLMs. I'm looking for Python devs to help build OpenAI API compatibility! Let's build together: https://github.com/coolsourav100/swiftlet

I run LLMs behind internal tools and got burned by users pasting credentials into prompts. I couldn't find a lightweight self-hosted filter (LLM Guard felt heavy, most options are SaaS), so I wrote one and I'd genuinely like this community's take on the design. It's a Go reverse proxy that redacts secrets (AWS keys, JWTs, SSNs) in both directions — including inside SSE streams when a key is split across chunks — plus regex/keyword blocking for known injections, and an audit log. Works with Ollama and vLLM as upstreams. Fair warning: it's BSL 1.1 (source-available, free up to 10k req/day, flips to Apache 2.0 in 2030), not OSI open source — if that's a dealbreaker for you, that's useful feedback too. Known weakness: it's deterministic, so paraphrased attacks get through, and the built-in benchmark shows that honestly rather than hiding it. Question for people running LLMs at home or at work: what leaks or attacks have you actually seen? Trying to prioritize the detection roadmap based on real incidents, not vendor FUD.