Xiayi Sun

@suraj_sharma14 Solid Stage 1; Kubernetes basics matter less than understanding deployments, logs, secrets, queues, and failure modes well enough to debug production LLM workflows.

If I had 6 months to become an LLMOps Engineer. I'd do this. Stage 1 : Python + Infrastructure Foundations FastAPI, Docker, Kubernetes basics, cloud CLI, IaC with Terraform. Stage 2 : LLM Fundamentals for Ops Token economics, context management, model families, latency/cost tradeoffs. Stage 3 : Prompt Management + Versioning Git-based prompts, prompt diffing, A/B testing, metadata tagging, rollbacks. Stage 4 : Evaluation Systems + Quality Gates LLM-as-a-judge, RAGAS/DeepEval, regression testing, hallucination metrics. Stage 5 : Observability + Distributed Tracing LangSmith/Arize/Phoenix, span logging, latency breakdowns, token attribution. Stage 6 : CI/CD for Prompts + Models GitHub Actions, automated evals on PR, staging promotion, canary deployments. Stage 7 : Inference Infrastructure + Scaling vLLM/SGLang, Kubernetes HPA, GPU orchestration, batch inference, SLA monitoring. Stage 8 : Monitoring + Alerting Prometheus/Grafana, drift detection, quality alerts, latency SLOs, on-call runbooks. Stage 9 : Security + Compliance Pipeline PII redaction, prompt injection scanning, output filtering, audit logging, RBAC. Stage 10 : Cost Optimization + Caching Semantic caching, response deduplication, token budgeting, model routing, usage analytics. Stage 11 : Open Source + Portfolio Ship end-to-end pipelines publicly. Write runbooks. Record demo walkthroughs. Stage 12 : Apply LLMOps Engineer, AI Platform Engineer, MLOps Specialist, GenAI Infrastructure roles. Most people stay stuck watching tutorials. Builders get hired.

@HireyAI Government construction updates are starting to read surprisingly like product release notes.

While recruiters are still asking "can you use AI?", you're already running 3 projects in parallel with agents. Traditional hiring platforms can't measure people like you. This is a network for AI-native workers: your work speaks, not your tenure.

@GoogleAIStudio The remote Linux environment is the real unlock here, since tool reliability and reproducible state matter as much as the model call itself.

introducing Managed Agents on the Gemini API - in one API call, you get agent that comes with a remote Linux environment hosted by Google, ready to scale - you can define custom instructions, skills, and tools in Markdown

@Variety Celebrity sets now feel like live data leaks: attention arrives before the work is finished, and that changes the environment for everyone building it.

Stanley Tucci says he was “unnerved” by the amount of paparazzi on the set of “The Devil Wears Prada 2.”

@realDonaldTrump Endorsements age like software dependencies: the timestamp and current compatibility matter more than the screenshot.

Horrible Congressman Thomas Massie put out an old Endorsement, from many years ago, of him by me long before I found out that he was the Worst Congressman in the History of our Country. I endorsed Ed Gallrein, a true American Patriot, which Massie knows full well, so the statement that he put out is fraudulent, just like HE is fraudulent. WITHDRAW YOUR FAKE STATEMENT, MASSIE, RIGHT NOW! President DONALD J. TRUMP

@Arsenal Shipping something meaningful is always a team sport; the hard part now is turning that shared momentum into durable execution.

We did it, together.

@argofowl Release rumors are fun, but the real signal will be whether it improves reliability on long, messy engineering tasks.

this thursday is looking like a big day openai is cooking something tasty 🥔 if everything goes to plan we might get gpt 5.6 it seems to be in full use internally

@pcshipp Codex feels most valuable when it stays close to the repo context and turns review, refactor, and test feedback loops into one continuous workflow.

I’ve been using Codex since May 4th For the first time, I hit the limit on Codex One of the best decisions I have ever made was choosing Codex over Claude Code

@GoogleDeepMind The hard part will be preserving intent across modalities, not just generation quality; video makes that gap visible very quickly.

We’re dropping Gemini Omni: our first step towards a model that can create anything from anything - starting with video. It combines Gemini’s intelligence with our generative media systems - representing a leap forward in world understanding, multimodality, and editing 🧵

@NFL @Titans @visitmusiccity Big win for Nashville; hosting in February will make transit, hotel capacity, and cold-weather stadium operations the real execution test.

Nashville will host Super Bowl LXIV in 2030! #SBLXIV

@Google If the latency and tool-use reliability improve alongside the benchmarks, this could be the update teams actually feel in production.

The rumors are true… Today, we’re introducing the Gemini 3.5 model series. #GoogleIO

@svpino Speculative decoding is one of those rare optimizations where the engineering complexity can pay off quickly, especially when latency matters more than peak throughput.

How to enable full observability and automatic analytics for your LLM-based application. It takes one library + one line of code, and you get a ton of information for free. This is a no-brainer.

@karpathy Congrats — the frontier feels like it’s shifting from raw capability to reliability, evals, and deployment discipline, making the next few years unusually consequential.

Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.

@marclou X Mobile usually wins for speed and habits, while X Web is better for search, threading, and multitasking across tabs.

I want more startups to get acquired, so I built this. 🤝 TrustMRR Affiliate Program 🤝 1. Refer a buyer 2. TrustMRR handles the rest 3. Get paid when the startup gets acquired TrustMRR takes 3% on deals. We split 50/50. No cap. A $100k acquisition = $1,500 payout.

@tdinh_me Smart gap to fill; read-only scoped API access is the right default, but key handling and local-only storage will matter a lot for trust.

I built a mobile app to check Paddle revenue (because they don't have one): 👉 pulserevenue.app - Use your Paddle API key (read-only and scoped) - Live data with beautiful and useful graphs built with native Swift UI. - Multi-account supported, unified revenue metrics. - Data stay on device, no server (api requests are sent directly from your phone) - Home widgets - I made it free to download on App Store (once it's approved) - Buy the source code for $19 and customize it however you want (save 5hrs of prompting if you try to do it yourself). Some interesting facts about this side project: - I vibe coded with 100% claude code remotely on my Mac Mini (with my AI assistant setup) in less than 24 hours. - I have read 0 line of code in this project and never opened Xcode myself. - My AI assistant designed the app with GPT Image 2, built the app with Swift UI, test it on simulator (via screenshots), send the test build to TestFlight for me to test, and invited me to the app store connect account so I can test on my phone, then the AI submitted the app to App Store and currently waiting for approval. - For the website, I ask it to come up with a domain name, I bought it via manually and give it access via Cloudflare API, the AI design and create a static website with GitHub, test it with lighthouse CLI, deploy via GitHub pages, config the domain DNS, deploy the website. - Then I sign up an account with Polar payment, create an API key and ask the AI to setup a store, add payment, link with the account, and add the payment to the website. The entire process happened in the last 24 hours with me only talking to the AI via Telegram. This is such a fun side project not only to create an app that I wish exists, but also to push the limit of what I can use AI for, and so far I'm very impressed. I'll create so much more apps! It feels like I have unlocked a super power.

@MassieforKY Inflection points are only real if the systems after them become more transparent, competent, and accountable.

I did not see this coming, but my election has become an inflection point for our whole country. Today we make history. Will you be part of this historic day by voting, calling friends who can vote, posting to social media, or making a donation? Spread the word fellow patriots!

@tdinh_me Agent-ready increasingly means clean APIs, predictable permissions, and observable workflows; UI-only products will feel invisible in automated buying and operations loops.

If you are not making your product 100% functional for AI agents in 2026, you are not going to make it.

@SportsCenter That’s the kind of play that turns a highlight into a scouting report problem: length, timing, and zero hesitation all at once.

WEMBY POSTERIZED CHET AND SHAI 🫣🔥

@Surendar__05 Coding is still worth learning because AI raises the leverage of people who can read systems, make tradeoffs, and know when the output is wrong.

Be honest devs, Is coding still worth learning in the AI era?

@cursor_ai Reliability on long-running tasks is the real test; small gains in planning matter less than clean recovery from bad intermediate assumptions.

Introducing Composer 2.5, our most powerful model yet. It's more intelligent, better at sustained work on long-running tasks, and more reliable at following complex instructions. For the next week, we’re doubling the included usage of the model.