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Zhoutong Fu
Hippocratic AI • 5K followers
It’s interesting to see the shift around reinforcement learning — it’s no longer about whether RL is the right approach, but how we can train it more efficiently and at scale. I’m expecting to see a lot more domain-specific applications pop up, both in public and private (enterprise) spaces. https://lnkd.in/gjc3RdcP
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Ben (Xiaojun) Li
Microsoft • 34K followers
Google Research Director Denny Zhou, who founded the LLM Reasoning Team at DeepMind, recently gave a great talk at Stanford’s CS25 class: Large Language Model Reasoning. This was one of the most intuitive talks about reasoning model training and application. He outlined new directions for training models to handle questions where answers aren’t easily verifiable. My team is currently working on an enterprise LLM project focused on using reasoning to extract relevant context and generate responses for unverifiable cases. It’s a tough but promising area, and it’s an interesting time to be building. [Link to Denny Zhou's talk: https://lnkd.in/gdJ6Mzi8]
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Julen Arizaga Echebarria
Meta • 3K followers
Bad press around autonomous driving is inevitable. Especially when a headline involves a child. Recently, Waymo faced intense scrutiny after one of its vehicles struck a child near a school. The child was thankfully only lightly injured, but the story spread fast, and the reaction was strong. As it should be. What’s getting less attention is the uncomfortable nuance. According to Waymo’s data, the system detected the child immediately, braked hard, and reduced speed significantly before impact. Their internal analysis suggests a typical human driver, even an attentive one, would likely have hit the child at a much higher speed given the same conditions. That does not make the incident acceptable. But it does challenge the way we frame these conversations. We tend to ask: “Did the autonomous system fail?” We rarely ask: “Compared to what baseline?” Human driving sets a very low bar. We just don’t notice it because human errors are normalized. The real question isn’t whether autonomous systems are perfect. They’re not. It’s whether they can consistently make fewer and less severe mistakes than humans, especially in chaotic, high-risk environments like school zones. Public scrutiny is necessary. Transparency is non-negotiable. But progress in safety often looks worse before it looks better, because machine mistakes are visible, logged, and headline-worthy in a way human mistakes never are. If we want safer streets, the comparison has to be honest.
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Antonio Mallia
Seltz • 4K followers
⚡ Exciting to see our Block-Max Pruning (BMP) technique in Infinity, an open-source AI-native database designed for LLM applications! In their latest VLDB paper, “Balancing the Blend: An Experimental Analysis of Trade-offs in Hybrid Search”, Hai Jin, Yingfeng Zhang, and co-authors present a rigorous evaluation of hybrid search architectures — combining full-text, sparse, dense, and tensor retrieval. To support efficient sparse vector search at scale, they’ve integrated BMP into Infinity’s SVS engine — a nice validation of our work on fast, top-k lexical retrieval. 🔗 BMP paper: https://lnkd.in/dsc33hGc 🔗 BMP code: https://lnkd.in/dxBxv225 🔗 Infinity: https://lnkd.in/ddRK5mbr 🔗 Hybrid Search paper: https://lnkd.in/dfBuDXmt Great to see ideas from traditional IR continuing to shape the next generation of retrieval infrastructure!
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Joseph Fuge
LocoLingua • 1K followers
The "Neural" in Neural Machine Translation (NMT) doesn't just refer to the act of translating at inference time. Modern Quality Evaluation or Quality Estimation (QE) systems are using straightforward metrics like BLEU, sacreBLEU, chrF, and more - but they also employ more recent Neural QE (NQE) metrics like COMET. With the right annotated data, such as human-annotated MQM scores on translations, COMET achieves impressive results even in quality estimation where a reference translation is not provided - this is especially critical if you want to provide a sort of "confidence score" to the end user consuming your machine translations, since those inference-time translations are unlikely to exactly match a training-set reference translation. Of course, human-annotated scores are hard to come by, and when you ask for bilingual annotators you likely want a high level of fluency to ensure the annotations are accurate. That kind of skilled work is likely expensive. Calling back to a previous post where I mentioned the potential applications of MT evaluation methods for LLM outputs, I wonder if human annotation of LLM outputs could be used to train an NQE system for GenAI rather than for an MT system. Testing that theory requires more expertise than I currently possess, and potentially a significant amount of man-hours to annotate LLM outputs, but I'll be curious to see where AI quality evaluation goes beyond simply throwing another LLM at the output of the first one. Speaking of "throwing another LLM at the output of the first one," some research has been done on using LLMs to assess translations. While they were effective at assessing the overall quality of a given MT system, individual "segment-level" translations were still better evaluated by other QE methods already mentioned - https://lnkd.in/g29mZuVU. I expect to see more progress in both LLM and MT evaluation, which will provide better quality outputs and better feedback to users, enhancing their control and confidence by informing them of the calibre of output they received. This may be a major distinguishing factor in GenAI and MT systems moving forward. Take care #machinetranslation
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freeCodeCamp
2M followers
If you're a Senior Engineer looking to move up, the next role will likely be a Staff Engineer. And in this guide, Shruti shares tips from her own experience of getting promoted to Staff Engineer at PayPal and Slack. She talks about what Staff Engineers do (and how it's different from Seniors), why you might not be getting promoted, and how to take that next step. https://lnkd.in/g58dnFEG
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Zohaib Khan
Panaversity • 1K followers
"𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐢𝐬 𝐛𝐞𝐢𝐧𝐠 𝐫𝐞𝐟𝐚𝐜𝐭𝐨𝐫𝐞𝐝." — Andrej Karpathy (Former Director of 𝐀𝐈 @ Tesla · Founding team @ OpenAI · CS231n / PhD @ Stanford) This line captures what many developers are feeling but haven't articulated yet. The role of a developer is shifting — 𝐟𝐫𝐨𝐦 𝐭𝐲𝐩𝐢𝐬𝐭 𝐭𝐨 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐨𝐫. We're no longer valued only for how fast we write code. We're increasingly valued for how well we design systems of intelligence. Agents. Sub-agents. Prompts, memory, tools, permissions, workflows. This is a new programmable layer — and it's changing everything. Now connect this with a real-world proof point: In 2023, Thomson Reuters acquired 𝐂𝐚𝐬𝐞𝐭𝐞𝐱𝐭 𝐟𝐨𝐫 $𝟔𝟓𝟎𝐌. Not for generic AI — but for 𝐞𝐧𝐜𝐨𝐝𝐞𝐝 𝐥𝐞𝐠𝐚𝐥 𝐞𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞. Their product, 𝐶𝑜𝐶𝑜𝑢𝑛𝑠𝑒𝑙, could: • Review legal documents • Research case law • Draft memos • Pass complex legal evaluations with ~97% accuracy They didn't buy "AI". They bought a 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐅𝐓𝐄 — expertise captured, systematized, and deployed 24/7. This is the 𝐀𝐠𝐞𝐧𝐭 𝐅𝐚𝐜𝐭𝐨𝐫𝐲 idea in practice: 𝐃𝐨𝐦𝐚𝐢𝐧 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 → 𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 → 𝐀𝐠𝐞𝐧𝐭𝐬 → 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐅𝐓𝐄𝐬 A Digital FTE isn't hype. It's a purpose-built agent that: • Works continuously • Executes a workflow reliably • Encodes rules, judgment, and constraints • Scales without linear hiring This explains Karpathy's deeper warning: ➤ Failing to claim the AI leverage is now a skill issue. The new skills aren't just frameworks or libraries. They are: • Specification-driven thinking • Agent orchestration • Tool & memory integration • Testing and evaluation for reliability • Turning knowledge into systems that do real work In this paradigm: • Code is no longer the product • 𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 • Developers become conductors • Agents become the orchestra This shift isn't documented in textbooks yet. Best practices are emerging in production, not tutorials. Those who learn by building will define the standards. The real question isn't: "Will AI change software development?" It's: 𝐖𝐢𝐥𝐥 𝐲𝐨𝐮 𝐛𝐞 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐅𝐓𝐄𝐬 — 𝐨𝐫 𝐜𝐨𝐦𝐩𝐞𝐭𝐢𝐧𝐠 𝐚𝐠𝐚𝐢𝐧𝐬𝐭 𝐭𝐡𝐞𝐦? #AgenticAI #AIAgents #DigitalFTE #AIEngineering #FutureOfProgramming #SoftwareEngineering #SpecDrivenDevelopment #LLM #LearningInPublic
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Raman Walia
Facebook • 34K followers
Why does an E3 level SWE at Meta make only ~190k/year, while an E8 level engineer makes over ~$2M/year, even though both engineers are ICs and spend the same time at work? I have spent the last 5 years at Meta as an IC. I joined with a little over 15 years of experience, and I’ve worked with many solid engineers in this time. Here is how I think about that compensation jump. 1. Same hours, completely different “unit of work” An E3’s unit of work is usually a task or a ticket. An E8’s unit of work is a multi year problem for the company. E3: “Implement this service, fix this bug, write this feature.” E8: “How do we cut infra cost by 20 percent across this product” or “How do we make this platform safe to scale to 10x users.” One person is paid to execute. The other is paid to decide what is worth executing in the first place. 2. Radius of impact E3 usually impacts a file, a service, maybe a small team. E8 shapes whole orgs and product lines. If an E3 ships something great, the impact is great but local. If an E8 ships the right platform, hundreds of engineers become faster and the company saves or earns millions every year. Comp tracks the area of the circle you influence. 3. Risk and downside protection At junior levels, mistakes are usually contained and reversible. At senior staff levels, a bad call can burn tens of millions or damage the brand. E8s are paid for judgment under ambiguity. They decide which bets the company should not make, which migrations can wait, which “shiny idea” is going to kill reliability. You pay more to people whose good judgment protects you from very expensive failures. 4. They scale themselves This can happen in a few ways. 1. Delegation with ownership They define the shape of the problem, then hand large pieces to other senior and mid level engineers while keeping the bar and direction clear. 2. Knowledge that travels They write RFCs, public comments, FAQs, wikis, internal posts. One answer helps hundreds of people who will face the same issue next quarter. 3. Tools over heroics Instead of unblocking people manually all day, they build tools, libraries or guardrails so others can unblock themselves. One well designed tool can save thousands of engineering hours every year. This is what “scaling yourself” actually looks like. The company pays for that multiplier. 5. Ownership of the “uncomfortable problems” Junior engineers usually work inside a well defined box. E8s take ownership between the boxes. They pick up problems that: Span many teams and no one really “owns” Require aligning leaders who disagree Have product, infra, legal and security angles at the same time Most people avoid those because they are messy, political and slow. Very senior ICs lean into them. That is where a lot of value sits. Continued ↓
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Julia Proskurnia
Google • 3K followers
Weekly arXiv Scan: 3 Papers that caught my eye (and my Agent’s) My custom Gemini x OpenClaw agent scanned recent papers on arXiv this week in Audio, NLP, and GenAI. It filtered for novelty and engineering rigor. I filtered for practical application and "human" impact. Here are the winners that made the cut during the baby nap time :) 1. Scaling Open Discrete Audio Foundation Models (SODA) https://lnkd.in/eXYnjfpd The Agent's TL;DR: Introduces SODA, a 4B parameter native audio model trained on interleaved semantic, acoustic, and text tokens, establishing new scaling laws for non-cascaded speech tasks. My Take: Human conversation relies on tone, pauses, and emotion. Those are the things that get stripped away when we convert Speech-to-Text for an LLM. By processing audio tokens natively, we aren't just processing data; we are preserving the humanity of the interaction. Native audio-in architecture is what we need for empathetic AI. 2. Reverso: Efficient Time Series Foundation Models https://lnkd.in/eyAgsN_h The Agent's TL;DR: Proposes a hybrid architecture interleaving linear RNNs and long convolutions that matches Transformer zero-shot forecasting performance while being 100x smaller in parameter count. My Take: Time-series analysis and prediction might be the problem where the sledgehammer solution is an overkill. A model that is 100x smaller is a model one can actually use for the personal gains (like some extra signals for investments? maybe?). It’s faster to train, cheaper to serve, and easier to debug. 3. A Generative-First Neural Audio Autoencoder https://lnkd.in/eeFtDpkB The Agent's TL;DR: Proposes an architecture with a massive 3360x temporal downsampling factor, allowing 60 seconds of audio to be represented by fewer than 800 tokens for extremely fast generation. My Take: High fidelity is great, but latency is the user experience killer. Compressing a minute of audio into <800 tokens is a massive engineering win. This reduces the context burden on the LLM significantly, meaning faster, cheaper, and more responsive voice agents. This is a great step towards the feeling of a snappy real-time conversations. The trend this week is “Efficiency over Excess”, finding architectures that do the same work with a fraction of the compute. #NLP #AudioAI #MachineLearning #HumanInTheLoop #arXivScan
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Lewis Won
A*STAR - Agency for Science… • 5K followers
Want to train LLM to reason right on your mac? Check out my article on Reinforcement Fine-Tuning LLMs with GRPO on Apple silicon using MLX framework. I remembered vividly when DeepSeek-R1 burst onto the scene this year, it dominated discussions over Chinese reunion dinners, AI stocks crashed, and friends I knew (including me) were busy asking it to reason their Bazi. A key idea introduced along with Deepseek R1 was Group Relative Policy Optimisation (GRPO), which helps to incentivise reasoning capability in LLMs via reinforcement learning. Since then, there have been courses such as Predibase's course on implementing GRPO, and improvements to the algorithm such as Mistral AI's Magistral LLM which was trained on a more efficient GRPO algorithm. Following the course by Predibase hosted on DeepLearning.AI, I have written an article where I explain step by step each component of GRPO equation, with a worked example to illustrate the algorithm. If you have on hand an Apple silicon computer, I have also included a Jupyter notebook which implements GRPO using Apple's MLX framework. Credits to Awni Hannun for creating the base notebook for LoRA Fine-Tuning with MLX LM. Full disclosure that I created the article and the notebook with the assistance of Google Gemini. If you noticed any errors with either the article or the notebook, please let me know or file a pull request.
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Benjamin Sammons
Zapier • 2K followers
One of my latest attempts to combat hallucinations in code is TypedMind, a concise language to model programs before or during implementation. It has a type checker to enforce rules for example that functions can’t use functions that don’t exist. Still early days, so I don’t use it all the time, and eventually I’d like to have language specific plugins so the checker can statically know if the real program complies with the TypedMind. Take a look! https://lnkd.in/gV_ZfWD9 - there is a CLI and VSCode extension for it as well. #ai #programming #hallucinations #anthropic #claude
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James Verbus
LinkedIn • 3K followers
🚀 New workshop recording: Reinforcement Learning for Orbital Transfers (Brown University Physics AI Winter School 2026) The Brown University Department of Physics / Center for the Fundamental Physics of the Universe just posted the public recordings from their 2026 AI Winter School, including my 2.5‑hour hands-on module on reinforcement learning (RL) for orbital transfers. In the session we: ▪️ Used Hohmann transfer as an analytic benchmark (minimum‑Δv two‑burn transfer under ideal assumptions) ▪️ Formulated the task as an RL problem (state / action / reward / termination) ▪️ Trained and debugged policies (discrete + continuous thrust), and analyzed classic failure modes ▪️ Compared learned trajectories vs. the analytic baseline using Δv efficiency + stability diagnostics This work bridges physics intuition, modern RL, and the practical workflow of problem framing + debugging. 🙏 Huge thanks to the Brown organizers for inviting me for a second year in a row, especially Ian Dell'Antonio, Rick Gaitskell, Ariel Green, and Chongwen Lu. If you’re curious, the recording, slides, and code (notebook) are now public (link in comments). #ReinforcementLearning #RL #BrownUniversity #Physics #AI
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Sita Lakshmi Sangameswaran
Google • 4K followers
✨ Feeling refreshed and energized after a vacation, and I'm excited to share 𝘁𝘄𝗼 𝗶𝗻-𝗱𝗲𝗽𝘁𝗵 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝘀𝗲𝘀𝘀𝗶𝗼𝗻𝘀 I had the pleasure of recording. If you're building with LLMs and AI agents, these deep-dives are for you. 🤖 𝟭) 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗔𝗗𝗞 & 𝗩𝗲𝗰𝘁𝗼𝗿 𝗦𝗲𝗮𝗿𝗰𝗵 with Kaz Sato 👉 Key Takeaways: How an Agent Development Kit (ADK) works with Vector Search to create sophisticated, production-ready AI systems. 👉 Beyond Basic RAG: We move past simple Q&A to discuss architectural patterns for building powerful semantic search and Retrieval-Augmented Generation (RAG) pipelines. 👉 Practical Implementation: A look at the code and components needed to bring these advanced search agents to life. 🔗 𝗪𝗮𝘁𝗰𝗵 𝗵𝗲𝗿𝗲: https://lnkd.in/gjKvq-MM 📈 𝟮) 𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁𝗢𝗽𝘀 with Dr. Sokratis Kartakis 👉 Key Takeaways: The "Day Two" Problem: Why traditional DevOps and observability tools fall short for monitoring complex, non-deterministic AI agents. 👉 Metrics That Matter: Learn the key metrics for tracking agent performance, cost, and reliability to ensure your agents are effective and efficient. 👉 A Framework for Reliability: Dr. Kartakis shares a practical framework for debugging, evaluating, and continuously improving your agents post-deployment. 🔗 𝗪𝗮𝘁𝗰𝗵 𝗵𝗲𝗿𝗲: https://lnkd.in/gpdC_Jk9 Which topic is more critical for you right now: building new agent capabilities or managing them in production? Let me know in the comments! #AI #GenerativeAI #LLM #VectorSearch #RAG #AgentOps #MLOps
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