50 Best Tweets About MLOps (2026)

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Akshay 🚀

@akshay_pachaar · May 11, 2026

All-time2.3KView on X

As an AI Engineer. Please learn: - Harness engineering, not just prompt engineering - Prompt caching vs. semantic caching tradeoffs - KV cache management at scale - Speculative decoding vs quantization - Structured output failures & fallback chains - Evals (LLM-as-judge + human… Read more

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Mayank Vora

@aiwithmayank · Mar 31, 2026

All-time397.6View on X

Holy shit…Someone built a production-grade LLM inference server that runs entirely on your Mac, persists KV cache across RAM and SSD so your AI never recomputes context it has already seen, and manages the whole thing from a menu bar icon. It's called oMLX and it turns your… Read more

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Tech with Mak

@techNmak · May 28, 2026

All-time299.2View on X

Someone documented the engineering principles behind AI agents that actually work in production. It's called 12-Factor Agents. Here's what each factor actually means and why it matters: Factor 1 - Natural Language to Tool Calls The LLM's only job is to decide what to do next,… Read more

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Ahmad

@TheAhmadOsman · Apr 3, 2026

All-time269.7View on X

You don’t pick an Inference Engine You pick a Hardware Strategy and the Engine follows Inference Engines Breakdown (Cheat Sheet at the bottom) > llama.cpp runs anywhere CPU, GPU, Mac, weird edge boxes best when VRAM is tight and RAM is plenty hybrid offload, GGUF,… Read more

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Tech with Mak

@techNmak · Jan 21, 2026

All-time217.1View on X

Our RAG system is 90% accurate. Sounds great until you realize: that 10% is destroying user trust. Here's what's happening: 9 out of 10 queries: Perfect answers. Users love it. 1 out of 10 queries: Complete hallucination. Users lose confidence. The trust problem with LLMs: >… Read more

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Aarno

@TheGlobalMinima · Jul 13, 2026

All-time134.6View on X

An awesome set of llm engineering and inference systems guides wrapped into one almanac by @modal learn how to > benchmark models > pick right framework > optimize latency, TTFT, ITL > serve types of workloads and a lot more. Good stuff! Also has a full playground to test and… Read more

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Suraj Sharma

@suraj_sharma14 · Jul 21, 2026

All-time132.9View on X

If I had to master Building Production-Grade AI Agents. I'd do this. Stage 1: Local-First Development Ollama, LM Studio, zero API costs, infinite testing, break things freely. Stage 2: Structured Outputs + Validation Pydantic schemas, JSON enforcement, retry loops, parse… Read more

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JustAnotherPM | Sid

@JustAnotherPM · Mar 31, 2026

All-time98.6View on X

𝗔𝗜 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗶𝘀 𝗼𝘃𝗲𝗿𝗵𝘆𝗽𝗲𝗱. That is what I told myself when I first started working on AI products. Turns out, I had no idea what an AI PM really did. After spending years watching world-class AI PMs, building AI products at scale, making 100s of bad decisions, I have a… Read more

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vas

@vasuman · May 1, 2026

All-time96.3View on X

The 5 principles for AI that ships to production: 1. Audit first: map the actual workflow before touching a model. Find the conformance gap. Separate repeatable patterns from genuine judgment. 2. Deterministic by default: LLM only where judgment lives. Code everywhere else.… Read more

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Leandro von Werra

@lvwerra · Mar 24, 2026

All-time94.9View on X

Auto-research for ML training models is all the rage now, but underrated is: auto-research for data! Sure, you can squeeze out a bit of model performance by optimizing hyperparameters, but code agents can do data work that has been very labour intensive and required a lot of… Read more

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Shalini Goyal

@goyalshaliniuk · Jul 30, 2026

All-time78.4View on X

Planning to build a GenAI or AI-powered product? Here’s the modern AI app stack you need to scale, serve, and secure your models. 👇 1. Data Layer: Foundation for AI Use Snowflake, BigQuery, Postgres, Airflow, and dbt to collect, clean, and move data efficiently. 2. Model… Read more

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Shalini Goyal

@goyalshaliniuk · Apr 7, 2026

All-time76.5View on X

Building an AI model isn’t just about training a neural network - it’s a full journey with 8 critical stages. From data collection to model monitoring, here’s how AI systems are built and maintained today: 1. Data Collection & Preparation Everything starts with data. Raw input… Read more

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Aurimas Griciūnas

@Aurimas_Gr · Apr 13, 2026

All-time76.4View on X

A breakdown of 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝗶𝗻 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 👇 And yes, it can also be used for LLM based systems! It is critical to ensure Data Quality and Integrity upstream of ML Training and Inference Pipelines, trying to do that in the downstream systems will cause unavoidable… Read more

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Alex Lieberman

@businessbarista · Jul 7, 2026

All-time74.5View on X

Loved this 22-minute talk on continual learning for AI agents. Must watch for anyone looking to get agents performant and into production. Credit: @FeiziSoheil at @aiDotEngineer • Agent learning can happen at three layers: the model (weights), the harness (prompts, tools,… Read more

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Aurimas Griciūnas

@Aurimas_Gr · Jun 14, 2026

All-time50.5View on X

Can you trust your agentic systems with the measurements you track? In my latest Newsletter episode I dig deeper into why and how agentic system monitoring differs from regular web services. I.e. what kind of additional metrics you should track as an AI Engineer building on top… Read more

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Jahir Sheikh

@jahirsheikh8 · Apr 10, 2026

All-time49.6View on X

Want to be a Strong AI Engineer (not just prompt user)? Please learn: 1. Transformer Internals: Self-attention math, KV caching, Positional encodings (RoPE, ALiBi), Scaling laws 2. Tokenization Deep Dive: BPE vs Unigram, Token distribution effects, Context window limits 3.… Read more

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Ashutosh Maheshwari

@asmah2107 · Mar 17, 2026

All-time49.0View on X

Every way I've seen AI agents fail in production. Bookmark this before you ship. 1. Silent context truncation mid-task 2. Tool call with no timeout 3. Hallucinated tool output treated as ground truth 4. No idempotency on retried actions 5. Cascading replanning loop 6. Missing… Read more

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Suryansh Tiwari

@Suryanshti777 · May 19, 2026

All-time44.9View on X

Most teams are building MCP servers the same way people built internal tools in 2019: one endpoint = one tool Then six months later the agent has 47 tools, picks the wrong one half the time, burns tokens routing requests, and nobody knows why latency exploded. The interesting… Read more

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agentX

@nikks_techie · Jul 5, 2026

All-time39.5View on X

AI tool of the day: LiteLLM What it is LiteLLM is an open-source gateway that provides a single, OpenAI-compatible API for 100+ LLMs and providers, including OpenAI, Anthropic, Gemini, Grok, Ollama, Bedrock, and Azure OpenAI. Benefits Switch between LLM providers with minimal… Read more

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Vivo

@vivoplt · Jul 18, 2026

All-time35.8View on X

As an AI Infrastructure Engineer. Please learn: - GPU/VRAM fundamentals, quantization & batching - vLLM / TensorRT-LLM / inference optimization - KV caching, speculative decoding & token throughput - Distributed training basics (DDP/FSDP/DeepSpeed) - Model serving & autoscaling… Read more

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Akshay 🚀

@akshay_pachaar · Apr 25, 2026

All-time35.4View on X

A great LLM interview question: (answer shared below) You have 80k Agent-user interactions from production. You need to find the top 100 worth reviewing to improve the agent. You cannot use an LLM to evaluate them since it will be expensive. This is one of the most painful… Read more

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Aarno

@TheGlobalMinima · Mar 17, 2026

All-time29.6View on X

The last 3 days have taught me a lot about how OpenTelemetry and Async Generator functions work. When you set up observability (personally using @langfuse ) for the first time, it is smooth sailing. But when you scale up your llm application to support concurrency, failure… Read more

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Dr Milan Milanović

@milan_milanovic · Mar 18, 2026

All-time25.5View on X

𝗧𝗵𝗲 𝗔𝘇𝘂𝗿𝗲 𝗔𝗜/𝗠𝗟 𝘀𝘁𝗮𝗰𝗸 Here are the most important Azure services if you want to work with AI in Azure. 𝟭. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 We can use Azure ML as the platform for managing experiments, compute clusters, and the model lifecycle. GPU VMs (NC/ND series) for training workloads that… Read more

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Priyanka Vergadia

@pvergadia · May 29, 2026

All-time20.5View on X

Most AI agents fail in prod. Not the model's fault. The 12-Factor Agents framework nails why and it's the engineering equivalent of "12-factor apps" but for LLMs. Here's the cheat sheet 1/ Own your prompts. Don't let a framework hide them from you. 2/ Own your context window.… Read more

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Turing Post (Ksenia Se)

@TheTuringPost · Jul 24, 2026

All-time17.8View on X

A must-read guide from @MongoDB -> Designing an Agentic Platform: The Infrastructure That Makes Agents Work https://t.co/C1kjFbyuLX It explains how the layers of the harness help AI agents survive in production Covers: - Why agents fail beyond the demo - Why models are the… Read more

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Suraj Sharma

@suraj_sharma14 · Jul 30, 2026

All-time14.5View on X

Demos get likes. Systems get paid. Stop building tutorials. Start building production. 1. Add evals before you write features 2. Track cost per request from day one 3. Implement retry logic with backoff 4. Add circuit breakers for API failures 5. Build fallback chains for model… Read more

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Harshil Tomar

@Hartdrawss · Jul 9, 2026

All-time14.2View on X

Build boring AI companies. I think it's the biggest opportunity of the next 10 years. 1. Every AI company needs clean data, but nobody wants to clean it. A data labeling service that guarantees 99.9% accuracy and charges per record, not per project. Boring, essential, recurring.… Read more

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Priyanka Vergadia

@pvergadia · Apr 7, 2026

All-time13.2View on X

NEW: The SDLC taught us software is correct or broken. AI just made that binary obsolete. → SDLC: write rules → test pass/fail → ship → done → AIDLC: collect data → train → evaluate statistically → monitor forever → Your model can be "healthy" and silently wrong at the same time… Read more

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Hasan Toor

@hasantoxr · Jul 2, 2026

All-time11.9View on X

llm-d is the next step after vLLM. Most teams still serve open models like this: Spin up vLLM. Put it behind an endpoint. Add more GPUs. Watch latency spike. Pay the bill anyway. This repo shows the next step: You stop treating inference like one model server. You build the… Read more

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Udayan Walvekar

@udayan_w · Mar 15, 2026

All-time11.7View on X

there's a reason some people's agents keep getting better and others stay stuck. it's not the model. it's not the prompt. it's something simpler. everyone optimising skills and memory for their agents right now is creating a feedback loop. most don't call it that. they call it… Read more

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Lior Alexander

@LiorOnAI · Jan 22, 2026

All-time11.2View on X

You can now ship a production AI agent in one command. Google just released Agent Starter Pack, and it cuts setup time to about 60 seconds. From empty folder to deployed service, with infra included. 𝗧𝗵𝗶𝘀 𝗿𝗲𝗺𝗼𝘃𝗲𝘀 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱𝗲𝘀𝘁 𝗽𝗮𝗿𝘁 𝗼𝗳 𝗮𝗴𝗲𝗻𝘁𝘀 Building logic was never the blocker.… Read more

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Abhishek Singh

@0xlelouch_ · Jul 23, 2026

All-time11.1View on X

90% of LLMOps in 2026 comes down to mastering these 10 concepts: 1) Problem framing + evals first. If you can’t define success with 50 to 500 test cases, you’re just shipping vibes. 2) RAG basics, but with rigor. Chunking, filters, metadata, and freshness matter more than which… Read more

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Alex Veremeyenko

@alex_verem · May 13, 2026

All-time8.5View on X

The AI is 5% of the work. The 95% that breaks: → Observability (Langfuse, Braintrust, Helicone) - you can't debug what you can't see → Evals - regression suites for non-deterministic software. The new CI. → Durable runtime (Temporal, Inngest) - so a 10-minute agent run survives… Read more

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Abhishek Singh

@0xlelouch_ · Jul 16, 2026

All-time8.2View on X

90% of LLMOps interviews in 2026 come down to these 7 points: 1) RAG system design: chunking, embeddings, top-k, rerankers, and how you measure retrieval quality vs latency. 2) Evaluation: offline golden sets + online A/B, judge model pitfalls, and metrics for hallucination,… Read more

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Harshil Tomar

@Hartdrawss · Jun 18, 2026

All-time8.0View on X

Sneak peek from a $10,000+ product we’re building right now ! Simply, infrastructure that lets teams deploy & manage agents in production with proper observability, and cost management For the core, we’re using : - LangGraph for stateful agent orchestration - Temporal .io for… Read more

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Ajay Yadav

@ATechAjay · Aug 7, 2026

All-time6.9View on X

An LLM can suggest an action. Your application decides whether that action is allowed. That's the difference between a demo and a production AI system. Everyone talks about models. Not enough people talk about guardrails. Guardrails are not the model. They're the… Read more

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Matt Dancho (Business Science)

@mdancho84 · May 1, 2026

All-time6.5View on X

The 7 levels of a production forecasting workflow: 1. Data ingestion — structured, repeatable, source-agnostic 2. Feature engineering — documented, inference-safe, no leakage 3. Model comparison — multiple candidates, same pipeline, honest evaluation 4. Backtesting — rolling… Read more

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Hugo Bowne-Anderson

@hugobowne · Apr 10, 2026

All-time6.5View on X

AI agents are failing silently in production, and it's costing companies tens of thousands of dollars before anyone notices. Here's what 1,400+ real deployments actually taught us: - The $50k infinite loop: agents confidently report success while spiralling into expensive… Read more

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divyansh tiwari

@DivyanshT91162 · Aug 15, 2026

All-time4.5View on X

The fastest way to understand LLM inference? Stop reading about it. Ship one. This 10-week roadmap takes you from a blank GPU to a production-grade, OpenAI-compatible inference service — in just 30 minutes a day. You’ll build your way through: → vLLM + SGLang →… Read more

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smrati tiwari

@smratitiwa86867 · Apr 12, 2026

All-time3.3View on X

Someone just open-sourced a real production AI system… Not a chatbot. Not a GPT wrapper. A full stack that actually scales. And it exposes the biggest lie in AI right now: → “Just call an LLM and you’re done.” Wrong. Real AI apps are built on: • Data pipelines (clean →… Read more

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Santiago

@svpino · Jun 22, 2026

All-time3.0View on X

You could make a living by helping companies fix their evals. You wouldn't need anything else: 1. Show me how you are evaluating your product 2. This is how you can make it better I'm always hearing the same story: • Someone picks a benchmark early on • Everyone becomes… Read more

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Vaishnavi

@_vmlops · Apr 28, 2026

All-time2.6View on X

THERE'S A LIVE MAP SHOWING THE CARBON FOOTPRINT OF ELECTRICITY ACROSS THE ENTIRE WORLD RIGHT NOW and most engineers have never seen it Electricity Maps tracks the carbon intensity of electricity in real time every 15 minutes across 190+ countries green zones mean clean energy.… Read more

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abdel

@AbdelStark · Jul 2, 2026

All-time2.3View on X

Starting my first QLoRA fine tuning pipeline on a A10G NVIDIA GPU via Modal. Sovereign agentic knowledge become extremely important. So I want to ramp up on being able to post train open weight models to build custom tailor made agentic workflows. Here I am starting from a base… Read more

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LlamaIndex 🦙

@llama_index · Apr 8, 2026

All-time2.3View on X

Common Failure Modes Break VLM-Powered OCR in Production. 🔁 Repetition Loops — model spirals into infinite whitespace, exhausts resources, cascades latency across your system 🛑 Recitation Errors — safety filters hard-stop legitimate extractions as "copyright violations" Same… Read more

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Mahesh Chulet

@mchulet · Jul 27, 2026

All-time1.8View on X

As an AI Engineer. Please learn >Harness engineering, not just prompt engineering >Context engineering, not just long prompts >Prompt caching vs. semantic caching tradeoffs >KV cache management, eviction, reuse, and memory pressure at scale >Prefill vs. decode latency and… Read more

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return of the research era ꙮ

@byebyescaling · Jan 6, 2026

All-time1.6View on X

THOUGHTS ON REFRAMING DIFFICULT AGENT ENGINEERING PROBLEMS IN PRODUCTION I have been working on some open ended and extremely difficult agent engineering problems in production. The main difficulties arise from trying to make the agents function effectively over long time… Read more

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Signal65

@Signal_65 · Jan 30, 2026

All-time0.73View on X

The Agentic AI Era Needs Different Benchmarks. As enterprise AI moves from simple chatbots to complex "agentic" workflows, traditional benchmarks are hitting a wall. They often suffer from data contamination (memorization) and fail to reflect real-world business tasks. The… Read more

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Krish Subramanian

@krishnan · Jul 19, 2026

All-time0.72View on X

The unglamorous part of AI is becoming the moat. Everyone is covering model launches. Netflix's new engineering writeup points to the harder Day 2 question: can you run LLMs like production infrastructure? Netflix says (https://t.co/tuYfXJNdnn) it runs the full LLM serving stack… Read more

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Towards Data Science

@TDataScience · Apr 13, 2026

All-time0.52View on X

"Most production ML models don’t decay smoothly — they fail in sudden, unpredictable shocks." Emmimal P Alexander zooms in on the reasons MLOps retraining schedules fail, and what we can do to tackle this recurring issue. https://t.co/MZGVsAkSFf

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Towards Data Science

@TDataScience · Apr 11, 2026

All-time0.23View on X

In her new MLOps deep dive, Emmimal P Alexander explains why calendar-based retraining fails in production, and how a practical shock-detection approach can work in real systems. https://t.co/MZGVsAkSFf