So, I just read LeCun's interview with Nebius Science. I feel he had some cool points about LLMs being able to answer things, but not literally understand the physics of the physical world. (Like, being able to explain a task and actually performing it are two completely different things.) But I wanted to get opinions on what others thought of his solution to the problem. He thinks JEPA could be the solution. But it made me think about whether JEPA is genuinely the architectural solution to this, or if we’re just looking for a "magic bullet" that doesn't exist yet in our toolbox I have the link here: https://nebius.science/stories/meet-yann-lecuns-lab-and-the-ai-world-of-2030 submitted by /u/ConsciousGreenPepper [link] [comments]

ASCIITermDraw-Bench: Can a Model Actually Draw in ASCII? Do we really need a image generator to relay our thoughts about - an architecture? a topology? a cluster og N nodes? Is it possible to let our AI assistants, easily absorb and understand and make possible changes easily relayed to them by us, the creators without much hassle? The answer could be: simple, plain-old ASCII images With this, introducing ASCIITermDraw, a benchmark with which we aim to evaluate SOTA Vision Language Models on their ability to follow instructions, recognize, and draw ASCII-based images. Most benchmarks focus on coding, mathematics, and reasoning, but ASCIITermDraw-Bench evaluates a different capability: whether a model can create accurate diagrams using only plain text, use ASCII -- freely. This is more difficult than it may seem. Models can often describe a diagram correctly, but arranging boxes, labels, connections, and arrows with precise layout is a separate challenge. The benchmark includes 80 tasks across four areas: Basic Box and layouts Network topologies Software architecture diagrams Image-conditioned diagram editing, where a model must modify a provided diagram while preserving everything it was not asked to change Tasks span multiple difficulty levels and follow a consistent format, making results comparable across categories and models. Evaluation Each response receives two scores: A structural score that verifies required labels, edges, entities, and relationships A semantic score produced by an LLM judge, evaluated five times per task to reduce judge variability Results are aggregated across all 80 tasks, with a 95% confidence interval calculated for the final score. This provides a more rigorous measure than relying on whether a diagram simply appears correct. The current leaderboard is: Gemma-4-31B-IT — 73.8% (±4.1) Qwen3.7-Plus — 70.2% (±4.6) Kimi-K2.6 — 61.8% (±6.0) MiniMax-M3 — 59.5% (±6.3) Qwen3.5-9B — 47.0% (±6.4) Ternary-Bonsai-27B — 45.9% (±7.1) Explore the Benchmark Twelve example tasks and the complete methodology are publicly available on Hugging Face. You can review the task format, examine the evaluation process, and run the benchmark yourself. Link submitted by /u/East-Muffin-6472 [link] [comments]

Hey any one experience overall score 2.66 and then Meta score 3 in some previous cycle ?? Or meta reviewer just rounded off 2.66 to 2.5?? Any Meta Reviewer here?? because there are some uninterested reviewers doing AI generated reviews and giving noisy scores. For them overall score gets lowered. submitted by /u/Historical_Pause247 [link] [comments]

As someone who studied stats undergrad and industrial engineering operations research grad, and who thinks about the practical business of ML components in software.... I get lost and a bit hopeless when I think about how to make useful software out of ML models in a reasonable amount of time, and in the current business environment. And when I look at the businesses where I have worked that have mountains of middle management running tiny bits of the ML model lifecycle (think feature extraction, data ingestion and integration, training infra, hosting infra, more hosting infra, applied science)... that only makes my head hurt even more. How do you go about making practical software out of ML components? Edit: I should mention that I mean from scratch ML components, not just a call to a third party hosted tool. submitted by /u/ConstructionBoth6461 [link] [comments]

GPT-2's vocabulary as a hyperbolic tree: 32,070 tokens inside a Poincaré ball that you can explore. Link : https://aethereos.net/static/tinny66666.html Link named after a reddit user disappointed with my 2D projection ... It uses the same data as the flat map, GPT-2-small's raw token embeddings and nothing else, but lays them out in hyperbolic space, where tree structures naturally fit. It runs on your phone. Drag to rotate, pinch to zoom, and tap any token to bring it to the center as the entire space shifts around it. This is a Möbius translation, the natural way to move through hyperbolic geometry. Tap neighbouring tokens to keep exploring. Why hyperbolic? The vocabulary's similarity structure forms a forest: one giant tree with about 2,300 tokens, a few hundred smaller family trees, and around 6,700 isolated tokens with no close relatives. Trees don't fit well in flat space, but they embed naturally in hyperbolic space, where available room grows exponentially with distance from the center. No optimisation or training is involved. The layout is constructed exactly. submitted by /u/Limp-Contest-7309 [link] [comments]

I'm a Computer Science student about to start my 4th semester this September in Pakistan. My long-term goals are: - Maintain a high GPA because I want to pursue a fully funded Master's abroad. - Eventually work at a top tech company (FAANG or similar). - Become a genuinely good software engineer rather than just someone who can build projects. A bit about me: I actually enjoy programming. I like logic, problem-solving, debugging, and understanding how things work under the hood. My initial plan for the rest of this year (August–December) was to focus on: - Java - Spring Boot - Backend development - LeetCode and DSA - SQL - System Design (starting with the basics) - Building projects and putting them on GitHub However, my brother (he's also studying CS) has a very different opinion. He's heavily into AI, automations, AI agents, and vibe coding. He told me that spending so much time learning to code deeply is becoming less valuable because AI can already generate entire applications. He even mentioned one of his friends vibe-coded a complex website with AI that was supposedly extremely secure and feature-rich. His argument is that I should focus more on AI workflows and automation instead of traditional software engineering. My opinion is a little different. I feel like AI is an amazing tool, but someone still has to understand: - Architecture - System Design - Databases - Security - Scalability - Performance - Debugging - Clean code - Software engineering principles My thinking is that AI can generate code, but it can't replace understanding why the code works or making good engineering decisions. Now I'm questioning whether I'm becoming outdated before I've even started. So I'd really appreciate advice from people already working in the industry. Some questions I'd love honest answers to: If you were a 4th-semester CS student in 2026, what would you spend the next 4–6 months learning? Is investing heavily in Java, Spring Boot, DSA, and backend development still worth it? How important is LeetCode today? Is it still necessary for top companies? Should I prioritize AI engineering, LLMs, agents, MCPs, and automations instead? If your goal was to maximize your career opportunities over the next 5–10 years, what roadmap would you follow? What skills do you think junior developers are overvaluing today, and what are they undervaluing? I'm not looking for motivational answers. If you think my plan is outdated, tell me. If you think it's solid, tell me why. If you think I'm missing something important, I'd genuinely like to know. I'd especially appreciate responses from senior engineers, hiring managers, or people currently working at large tech companies. Thanks in advance! submitted by /u/Few-Pilot7575 [link] [comments]

32,070 alphabetic tokens from GPT-2-small's WTE, no forward pass and no context. Works on mobile. Pinch to zoom, tap a token to see its nearest connections, tap a neighbour to walk the graph. Search box to jump anywhere. Layout is t-SNE over a compressed representation of the embedding table; edges are a minimum spanning tree in that space, so every line is a real nearest-kin relationship, submitted by /u/Limp-Contest-7309 [link] [comments]

This visualization looks at the token “Trump” in GPT-2 Small’s static embedding table, before attention or context is applied. The top plot is a t-SNE projection of 32,070 alphabetic tokens with at least two characters. The two graphs below compare Trump’s nearest neighbours under two representations of the same embedding: Discretized: each coordinate is thresholded before neighbours are calculated. This produces mostly generic political terms such as Mitt, Hillary, Pelosi, and Blair. Continuous: the original coordinates are retained. This produces a more specific group containing family members, staff, rivals, and presidents including Obama, Clinton, Bush, and Eisenhower. No prompting or text generation is involved; everything comes directly from GPT-2 Small’s learned token embeddings. submitted by /u/Limp-Contest-7309 [link] [comments]

Hello, I recently finished reading this survey paper: Deep learning tackles single-cell analysis – A survey of deep learning for scRNA-seq analysis, which comprehensively covers 25 different methods across 6 subcategories for applying deep learning to scRNA-seq analysis. To summarize the methods from this paper, I prepared a table containing the Category, Method, Purpose, Architecture, Metrics, Explanation, and the specific Novelty of each method. I hope you find this summary useful. https://preview.redd.it/n3okgq66t1eh1.png?width=1662&format=png&auto=webp&s=fb71b306d3ffc346f243b04ccc9fe7a14076fbd7 https://preview.redd.it/njs5lq66t1eh1.png?width=1662&format=png&auto=webp&s=1dcdebbef3f2746b1e94f7182c9e4a11f10a7f12 https://preview.redd.it/zsv2yq66t1eh1.png?width=1662&format=png&auto=webp&s=05ecb58355b6a32a25d32cc25a2a9e500f9cd489 submitted by /u/teraRockstar [link] [comments]

The Google DeepMind-sponsored Kaggle challenge "Measuring Progress Toward AGI - Cognitive Abilities" asked participants to design new cognitive-science-based AI benchmarks and they just announced the results this week. In my two posts I present evidence that deepmind & kaggle rewarded a nonsensical number generation machine and a litany of unfounded claims with 25k and a grand prize stamp. What the authors of the work I analyze intended to do was to present an LLM with alternative viewpoints of other LLMs on 5 claims regarding a tricky situation and see whether the model changes its own assessment. It's an interesting question. However, it turned into a vibed pile of spaghetti 10 times the size of the requested submission format which it seems neither the authors nor the judges were able to (or minded to?) give a cursory reading. Here's the original posts in the competition forum, if you are looking for some AI research slop detective work / rant please help yourselves. But beware, some of the "universal findings" or "core insights" of the authors might continue to haunt you. You might even question your own sanity (as I did). Part 1: The Smoke: cursory review of the writeup Part 2: The Fire: looking at the methodology, code, and data The organizers' stance has been that review was done properly and this is just a matter of subjectivity. What do you think? submitted by /u/TheWerkmeister [link] [comments]

How to submit to AI alignment track? I can only see these at openReview: AAAI 2027 AAAI 2027 Artificial Intelligence for Social Impact Track AAAI 2027 Conference AAAI 2027 Innovative Applications of AI submitted by /u/Silencer_Wasd [link] [comments]

I built a small web app that lets you run tabular foundation models (currently just Google's TabFM) on spreadsheets without writing any code. Just drop in a CSV/Excel file, click a column header to mark what to predict, hit predict. Rows where the target cell is filled become the in-context examples and empty ones get predicted, right on the grid. A lot of people who'd benefit from these models aren't programmers, so I wrapped it in a UI anyone can use 🙂 Repo: https://github.com/LckyLke/TabFMLabs Feedback very welcome! submitted by /u/Lckylke [link] [comments]

Started testing a private qwen 35B moe capacity LLM runtime on s26 ultra, early testing shows that active model footprint can fit within the device’s memory limits.( not sharing the methods or architecture used) and results suggest roughly 90 input processing t/s achievable after optimisation and output generation is around 8 tokens/s on this mobile. Point is i learned ai ml based on my interest and no formal PhD , I have compute and resources to test. Anyone willing to join or collab to test on this I tried publishing papers on arxiv and 4 papers are still on hold as im first author and from no institution... submitted by /u/Severe_Post_2751 [link] [comments]

I have released a model that I have been working on for ~6 months off and on. I've been enjoying listening to spatial music, but there is a lot of music out there with no real quality spatial mix. So I decided to make a model to convert stereo to spatial. I started by making a flow-matching diffusion model that operated in the latent space of a separately trained VAE (EAR-VAE). I used the VAE to encode the stereo input in to 1 latent and then separately on each channel of the 7.1.4 output. This design is similar to a paper I found about another stereo -> spatial project (ImmersiveFlow (they have code links in the paper but they 404 for me so I made the codebase from scratch)). One key addition I made over the ImmersiveFlow paper was a way for the model to carry state (memory tokens) across windows to enable stable long context generations. Since the VAE was out of distribution with the output (was trained to encode\decode stereo tracks directly, not individual channels and certainly not individual channels of a 7.1.4 mix), the results hit real quality bottlenecks. But it showed that the mapping was possible. Rather than stick to modeling in latent space, I decided to pivot to modeling raw waveforms. This ended up fixing all of the quality issues I was seeing with the latent version, but at the cost of more training compute and instability. The waveform version of the model would train to around 60K-80K steps with loss going down like normal, validation generations looking good\improving, and then it would quickly become unstable and the loss would shoot up. I tried direct waveforms, scaling the waveforms by a multiplier, aggressive grad clipping, lower learning rates, and all of those experiments failed the same way. Luckily, I came across a recent paper called WavFlow. The authors of this project also had issues with modeling raw waveforms with flow-matching diffusion, and they solved it by using amplitude lifting (scale each audio track to an rms of 0.33 then multiply by 3) (the WavFlow paper used a clip of 1.0 before the scale which leads to a model space of -3 - 3, but I used a clip of 4.0 which leads to a model space of -12 - 12 (from my testing this led to better binaural outputs)). Once I implemented amplitude lifting like the paper, the training stability issues vanished. The waveform model was trained on 7,669 tracks for ~20 days on 2x A6000 gpus - 10 days stage one, effective batch of 16, 10- 18- 26- and 34-second training sequences - 10 days stage two, effective batch of 16, 122-second training sequences - There is optional mix-style conditioning for controllable outputs (waveform version only) - Direct binaural output (waveform training is more compute expensive than latent training so binaural proves the idea\theory and the same codebase and data can be used in the future to train a 7.1.4 version (once I have access to the compute)) I also made a Windows desktop app for inferencing with the model and consuming the results. Everything has been released apache 2.0. Huggingface link for waveform model: https://huggingface.co/francislabounty/stereo2spatial-v2-binaural Huggingface link for latent model: https://huggingface.co/francislabounty/stereo2spatial-v1 Github repo for training\inferencing: https://github.com/francislabountyjr/stereo2spatial Github repo for the Windows app: https://github.com/francislabountyjr/stereo2spatial-app App Homepage: https://stereo2spatial.francislabounty.com Case study for the waveform model: https://francislabounty.com/blog/stereo2spatial-v2 Case study for the latent model: https://francislabounty.com/blog/stereo2spatial Playlist of generations made from the model (will be updated with more tracks over time): https://www.youtube.com/playlist?list=PLQ-HHjPijrAg If you have any questions\etc. leave a comment and I will do my best to get them answered! Disclaimer: I know AI written posts are generally frowned upon so this post was 100% written by me (mistakes and all). But the case studies and READMEs from the above links were made with AI assistance. submitted by /u/kittenkrazy [link] [comments]

Does anyone have accepted short-paper at ACL/EMNLP/EACL 2025/26? Could you share your track and overall assessment? I'm just trying to get a sense of things, as it seems short papers have a lower acceptance rate than long ones. submitted by /u/No_Cardiologist7609 [link] [comments]

In this post, we walk through a few ways that Quick delivers on this promise. We cover the entire sales cycle, from identifying your highest-priority prospect, contacting them, working the deal to close, and keeping the CRM up to date as the account matures, while protecting your scarcest resource: your time.

Just found out from Prism's Discord that compiling is returning someone else's paper. There's a Twitter post too. https://x.com/JustanOthRando/status/2078169169267482778?s=20 I commend their prompt response, though. They took the website down within 10 minutes of the first time the bug was flagged. Just worried if my paper maybe somewhere out there. https://preview.redd.it/csr59ogtwtdh1.png?width=876&format=png&auto=webp&s=8a7d431a98005a7fb0910e7e8f5ed79077bb70b9 submitted by /u/Few-Monitor5103 [link] [comments]

Teams that rely on dashboards for daily decisions often must pinch and zoom to interact with controls originally designed for larger displays. Checking revenue during a morning standup, reviewing pipeline metrics between meetings, or monitoring operations while traveling all require extra effort when the dashboard was built for a desktop screen. Mobile Layout for Amazon

In this post, we cover a high-level view of the Smartsheet remote MCP architecture, with a focus on the AWS infrastructure behind it. This includes security, governance, scaling and deployment, and the AI-specific optimizations Smartsheet built on AWS.

I submitted my TACL paper approx on June 1th and was scheduled for July 1st cycle, when and how do you guys think we'll be getting our reviews given the July cycle for the paper which I've submitted at TACL ? And how long does the entire process take for those who have submitted to TACL ? Also, I do want to ask, how good is TACL as a journal and how respectable or how is a TACL publication viewed ? submitted by /u/Practical-Buddy6323 [link] [comments]

I have released EU AI Act OpenRAG, a downloadable corpus of Regulation (EU) 2024/1689 designed for RAG and legal-NLP experimentation. Instead of sliding character windows, the corpus chunks on the Regulation’s legal structure: one chunk per article paragraph one per recital one per Article 3 definition one per annex point chapter, section and provision metadata stored separately The resulting SQLite database contains 933 chunks and a normalized 1024-dimensional BGE-M3 embedding for every chunk. It also includes exact EUR-Lex links, Article 113 application-date metadata and deliberately narrow derived labels. Direct textual classification is stored separately from broader regulatory-regime association, and ambiguous cases remain NULL. I evaluated it against the AI Act Evaluation Benchmark using a like-for-like whole-unit baseline: scenario article recall@20: 0.541 structural vs 0.449 baseline QA article hit@10: 0.927 structural vs 0.898 baseline overall RAG classification remained close and was slightly lower on the structural corpus, suggesting that generator behaviour dominates that task more than chunk granularity I have published the full results, limitations, derivation methodology, label audit and licensing breakdown rather than only the favourable metrics. Dataset: huggingface.co/datasets/faitholopade/aiact-openrag I would appreciate technical feedback, particularly on the retrieval evaluation, structural chunking methodology and what additional baselines would be most useful. submitted by /u/Automatic-Forever-63 [link] [comments]

In this post, we walk through the three pillars that make this possible: simplified setup, smarter retrieval, and production readiness. We also show you code examples for setting up a knowledge base and retrieving from it.

i'm subscribed to a ai/ml newsletter but i feel like its not enough. i need a complete and not too time consuming way to keep up with ai/ml news because i feel like im left behind. thanks in advance submitted by /u/mehmetflix_ [link] [comments]

This post covers what makes Grok 4.3 a great fit for agentic and enterprise workloads, how you access it through Amazon Bedrock, and how to use the capabilities most teams reach for first: a basic chat request, configurable reasoning effort, tool calling, structured output, image input, and stateful multi-turn conversations.

Hi everyone, I've been working independently on a recurrent architecture called **DABSN (Dynamic Adaptive Bias State Network)** for the past several months, and I finally reached the point where I feel comfortable sharing the first preprint. The paper is mainly about the architecture itself and its behavior on reasoning, memory, and long-sequence benchmarks (MQAR, Copy, Key-Value retrieval, A5/60, etc.). The code is also public with PyTorch, C++, and Triton implementations so everything can be reproduced. While finishing the paper, I also trained my first language model with the same cell: - 24M parameters - 1B pretraining tokens - GPT-2 tokenizer Those results ended up being much more interesting than I expected, so I'm now writing a second paper focused entirely on language modeling, long-context behavior, and scaling. This is where I'd love some help. I'm looking for people who might be interested in collaborating on the next paper, whether that's: - independent reproduction of the results, - helping design stronger baselines and evaluations, - or having access to larger GPU clusters so we can scale the architecture much further than I can on my own. Everything I'm doing is intended to be open and reproducible from day one. I'd really appreciate any feedback on the paper, and if the project sounds interesting, I'd love to chat. Preprint and Github are in the comments. submitted by /u/BleedingXiko [link] [comments]

While writing an essay about AI memory and persistent context, I started wondering whether current AI memory systems are optimized for the right thing. Current AI systems already maintain forms of persistent context through saved memories, conversation summaries, user preferences, project notes, and similar mechanisms. These memories are primarily descriptive. They help the system remember facts about the user and previous interactions. But suppose future systems evolved in a different direction. Instead of primarily storing facts and preferences, imagine the persistent context being continuously refined and restructured to infer higher-level patterns such as recurring explanatory frameworks, preferred abstractions, and characteristic reasoning styles. For example, rather than remembering: "This user is interested in economics." "This user works in engineering." the system might gradually infer: "This user tends to explain economic outcomes through incentives and institutional constraints." "This user tends to understand complex systems through interactions and feedback loops rather than by analyzing individual components in isolation." In such a system, persistent context would become less like a collection of notes and more like an evolving model of how the user understands and interprets problems. Could representations like this emerge naturally from sufficiently capable AI systems, or would they require architectures fundamentally different from today's memory, retrieval, and summarization approaches? submitted by /u/Boris_Ljevar [link] [comments]

https://arxiv.org/pdf/2607.13511 the core idea is, we cannot have ternary PTQ with fixed matrix size, trying to do that is dead end. so i tried decomposing the matrix to 2 ternary matrices and inner diagonal scaling matrix. now that the inner rank can be arbitrarily large the accuracy can be arbiratily small. and its not that it has to be very large too i also showed that it does take only slightly more vram then current quantisation methods. the slight more vram is worth it if we abuse the ternary math. submitted by /u/LMTLS5 [link] [comments]

Just saw the ECCV registration fees and I'm shocked, student registration is 440 USD for early bird, and the worst thing is that you can't even do the student registration if you're presenting a paper there, a paper has to be covered by a FULL registration which is 805 USD How are they literally punishing us for getting a paper accepted? We even applied for travel grant and a registration waiver as students just to get rejected. Is there anything we can do? Some advice would be really helpful submitted by /u/NotGondor [link] [comments]

Built partnered with the AWS Generative AI Innovation Center (GenAIIC), AWS Partner AND Digital, and AWS account teams to create a scalable, AI-powered document processing engine that can classify, split, extract, evaluate, and reason over complex real estate finance documents. It reduces workflows that previously took days to minutes, supports hundreds of document types, and gives technical teams and industry experts a shared environment for building and improving document processors.

In this post, we walk you through the Computer Vision MCP Server, which illustrates this approach, representing how AI systems can process visual information and make intelligent decisions through a single, standardized interface. This convergence transforms what was once a complex integration challenge into a streamlined process, making AI capabilities accessible to a broader range of applications and developers.

In this post, we present a solution designed to centralize the monitoring of SageMaker Pipelines across AWS accounts and Regions using Amazon CloudWatch custom dashboards. The accompanying GitHub repository provides a customizable AWS Cloud Development Kit (AWS CDK) example of the required infrastructure.

This post shows how Thrad.ai deployed a multi-agent system with Strands Agents and Amazon Bedrock AgentCore that automates the pipeline from prospect discovery through personalized email generation. The post compares two orchestration patterns (Swarm and Graph) with head-to-head benchmarks on latency, cost, and email quality. You’ll also learn how the system scores prospects using weighted criteria, intent classification, and temporal decay, plus governance controls for production deployment.

In this post, we extend that foundation to demonstrate how QA Studio addresses batch regression testing and pipeline integration through test suites that organize and parallelize execution, and a command-line interface that brings agentic testing into automated CI/CD pipelines.

Using generative AI enables parallel execution of comprehensive user flow testing at scale. This solution demonstrates how to build a cloud-deployed UX testing platform that automatically generates test scenarios from documentation, executes user flows at scale using the intelligent navigation capabilities of Nova Act, and provides actionable insights through automated analysis.

In this post, we share how Flo Health’s engineering team turned a proof of concept (PoC) from the AWS Generative AI Innovation Center into a production-grade, AI-powered medical content review and generation system built on Amazon Bedrock. T

In this post, you will learn how ScienceSoft, an Amazon Web Services (AWS) Services Partner, integrated Amazon Nova 2 Sonic with Amazon Bedrock Guardrails to build a Health Insurance Portability and Accountability Act (HIPAA)-compliant AI voice scheduler. You will see how the solution addresses healthcare scheduling challenges while maintaining privacy, compliance, and responsible AI standards, and how you can apply the same architecture to your own workflows.

In this article, you will learn how to evaluate LLM applications using the three dominant open-source frameworks — RAGAS, DeepEval, and Promptfoo — and why...

Today, GPT-5.6 Sol, Terra, and Luna from OpenAI are generally available on Amazon Bedrock, bringing the smartest family of models from OpenAI yet to Amazon Bedrock’s next-generation inference engine built for high-performance, security and reliability.

In this post, I share how AI serves as an accessibility tool for neurodivergent professionals. The system is built on Amazon Quick on your desktop, an AI-powered desktop and web assistant that compensates for executive function gaps every day.

In this post, we describe how Bluesight used two AWS engagements and Amazon Bedrock AgentCore to evolve from a single-product AI prototype to Prism, a unified agentic AI solution spanning six healthcare compliance products. Prism Assistant for ControlCheck launched in May 2026 and is already in use by 20 health systems. A more complex multi-product agentic solution is on track for later in 2026.

Building multi-tenant agents with Amazon Bedrock AgentCore and Apply fine-grained access control with Bedrock AgentCore Gateway interceptors establish the conceptual foundation for on-behalf-of (OBO) token exchange in agentic systems. This post is the implementation guide. It walks through a complete multi-tenant OBO setup against Okta, shows the JSON Web Token (JWT) claim transformations on each hop, and demonstrates how audience binding produces defense in depth that scales across tenants.

In this post, we introduce the UI for optimized generative AI inference recommendations in Amazon SageMaker AI Studio, a low-code no-code (LCNC) experience. The API already gives you programmatic access to recommendations, but it assumes you know which parameters to set and how to interpret raw benchmark output. The UI removes that assumption. It guides you through preset use-case profiles, visual comparisons of results, and one-click deployment, so teams without deep infrastructure expertise can get a validated configuration on their own.

Agent systems change constantly in production.

In this article, you will learn how to choose the right memory strategy for an AI agent by working through a simple decision tree, one...

In this post, we explore what makes the Nemotron 3 architecture unique, walk through the fine-tuning techniques available, and show you step-by-step how to get started with serverless customization using SageMaker Studio.

This post describes how Henry Schein One closed that gap by building Image Verify, an AI-powered quality verification system on Amazon SageMaker AI that evaluates dental X-ray quality at the point of capture, in real time, across thousands of locations. The system went from concept to over 10,000 active locations within months and has already processed over 11 million X-rays and growing at 1.5 million per week. Henry Schein One is now scaling toward 40,000 locations globally across four regions.

The default assumption in most LLM developer communities is that you start with raw API calls and graduate to a framework as your project grows.

Tools execute code.

You build an agent with five tools.

Compression on Arrival Tool outputs should be compressed after a call returns, not after the window fills.

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For Job Postings please use this template Hiring: [Location], Salary:[], [Remote | Relocation], [Full Time | Contract | Part Time] and [Brief overview, what you're looking for] For Those looking for jobs please use this template Want to be Hired: [Location], Salary Expectation:[], [Remote | Relocation], [Full Time | Contract | Part Time] Resume: [Link to resume] and [Brief overview, what you're looking for] ​ Please remember that this community is geared towards those with experience. submitted by /u/AutoModerator [link] [comments]

In this article, you will learn five practical strategies for managing context windows in long-running AI agent applications, along with the key tradeoffs each approach...

MCP provides a standard way for AI applications and external systems to communicate.

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