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Hacker News Show HN

Show HN: Realtime sync engine without any new infra

Tandem is an open-source sync engine and client-side database that plugs into your existing infrastructure. Add the Tandem library to sync data in real time across your clients and server, without deploying any new services.I started work on Tandem 6 years ago as a side project. I was frustrated by the sync engine landscape at the time, thinking I could build a better DX and require less lock-in. Even now, sync engine companies and “full-stack databases” need you to migrate to their database or provision a separate service with auth, networking, etc. This makes them annoying to maintain and also harder to adopt incrementally.Tandem is designed to just be embedded into your existing frontend and backend code. You define a schema, set up a database adapter (any SQL-like database is supported), add the new sync endpoint to your existing API, and then you're good to go.Tandem uses DST (Deterministic Simulation Testing) on every PR and nightly to ensure correctness. Our DST system Gatekeeper lets us model not only storage and network failures, but also race conditions, by gating each call between client/server/other services independently.Tandem currently supports any React-based frontend, any serverful backend (like Cloudflare Durable Objects), and any SQL-like database. New adapters are easy to build, and PRs and issues are appreciated!Open-source and MIT licensed: https://github.com/tanishqkancharla/tandem Comments URL: https://news.ycombinator.com/item?id=50398763 Points: 1 # Comments: 0

The Verge · AI

Why a new report on Anthropic automating AI development is alarming researchers

Anthropic is using AI to build more powerful AI. And it appears to be working remarkably well, with the company's models improving roughly twice as quickly as a year ago, with little indication of slowing down. That's according to a new report published Thursday by the AI safety research organization METR, following an 11-week investigation […]

MarkTechPost

Zyphra ZYFA: FP8 FlashAttention for AMD MI355X, Up to 1.76x Faster

Zyphra released ZYFA, open-source FP8 and BF16 prefill attention kernels for AMD Instinct MI355X. On real model activations, its FP8 kernels run 1.16-1.76x faster than AMD’s AITER, with 62-78% lower error at head dimension 128. The post Zyphra ZYFA: FP8 FlashAttention for AMD MI355X, Up to 1.76x Faster appeared first on MarkTechPost.

LangChain

Agents Are Going Async

Async subagents let an agent stay responsive while background workers run long tasks. How the orchestrator, notifications and approvals work in Deep Agents.

arXiv cs.AI

Closed-Loop Agent Evaluation: Layered Measurement and Self-Calibration

arXiv:2611.07051v1 Announce Type: new Abstract: Once an agent is live, teams want to know whether it is doing its job, not just whether it throws errors. Yet most evaluation is one-off: a benchmark run, a look at traces, a single score. Its conclusions neither check themselves nor reach the agent. We propose closed-loop evaluation, an evaluation method for deployed agents. It models evaluation as performance estimation under biased raters and censored outcomes, and it requires every level's conclusion to trace back to atomic data and to be computed by versioned rules. The method covers four levels of analysis (span, trace, conversation and engagement), measures along a spine of Execution, Substance, Trajectory and Results, treats Conduct as a constraint throughout, and uses Provenance and Adjudication components to certify the evidence and the evaluators. Evaluator agents generated by an Agent Factory carry these components, each with a domain knowledge pack and an adapter harness. Two loops close the evaluation: an inner loop audits the evidence, rules and metrics themselves, and an outer loop uses per-component regression checks to drive agent improvement. We also give a pipeline that runs this process automatically. Using the business-analysis agent SharpInsight as a case and porting the method to public conversation data, we show how problems differ across levels and how evidence-and-rule judgment is more reliable than holistic scoring (ICC 0.86 vs. 0.81; kappa 0.81 vs. 0.63).

AWS Machine Learning Blog

How Amazon Marketplace evaluates content quality with Amazon SageMaker AI

Learn how Amazon Marketplace evaluates A+ Content quality at scale on Amazon SageMaker AI by rendering each product page as a single snapshot, scoring it with a vision-language model fused with OCR readability signals in a CatBoost classifier, and using SHAP to turn each decision into prioritized, actionable recommendations for selling partners.

arXiv cs.LG

LowRAM: Low-Rank Keys and Amortised Values for Composable KV Cache Compression at Test-Time

arXiv:2611.07025v1 Announce Type: new Abstract: Autoregressive decoding relies on the KV cache, whose memory footprint grows linearly with context length and caps batch size and throughput. Existing post-training methods exploit redundancy across tokens, layers, and feature dimensions, or lower numerical precision. We identify a complementary axis: within each attention head, values are largely predictable from their corresponding keys. We exploit this with value cache amortisation, which fits a lightweight per-head predictor at test time and stores residuals only for the tokens it reconstructs worst, giving $4\times$ near-lossless value compression. The predictor can be initialised by meta-learning for substantially reduced prefill overhead, or fitted from scratch in settings that allow a one-off cost, such as multi-query inference. We pair value amortisation with cross-layer low-rank key compression to produce LowRAM, an asymmetric compression pipeline that stays close to lossless across three model families. By reconstructing only a selected subset of KV pairs at each decoding step, LowRAM can keep decoding overhead minimal and achieve up to $5\times$ higher peak decode throughput at $4\times$ compression. LowRAM further composes with quantisation and eviction, reaching $26.9\times$ compression at $94.5\%$ of baseline performance on long contexts. These results show that treating keys and values as structurally distinct enables efficient, aggressive, and composable KV cache compression.

SiliconANGLE AI

Oracle wants to pull stalled AI agents out of demo mode and into production

Companies want AI agents faster than they can build the AI architecture to run them. Most can put together a convincing demo. Far fewer can move agents into production without exposing data, hurting uptime or blowing their budgets. That gap is pushing database providers to rethink what a data platform for agents has to deliver. […] The post Oracle wants to pull stalled AI agents out of demo mode and into production appeared first on SiliconANGLE.

SiliconANGLE AI

Same model, better answer: Enterprise data is what turns generic AI into a business tool

Generic AI models can write SQL, but they don’t know a company’s orders, customers or revenue definitions. Now, Oracle Database aims to give AI agents the context to move a stalled order forward. But that shift raises questions about complexity, lock-in and security as AI agents start acting on live transactions. Oracle Corp.’s answer is […] The post Same model, better answer: Enterprise data is what turns generic AI into a business tool appeared first on SiliconANGLE.

Hacker News Show HN

Show HN: Cerea – yet another self-hosted LLM chat front end

Here is Cerea, a self-hostable, open-source (Apache 2.0) chat+agent frontend for LLMs. It works alone with any OpenAI-compatible inference provider, but it's way better when coupled with the model gateway I'm also developing, Pystino (https://github.com/paoloviviani/Pystino).I came to this after struggling to find a hosted chat interface that was truly open source, that I really enjoyed to use, and that was simple enough to be shared and used by family. In particular, I was looking for something that could provide the smooth and integrated user experience of commercial products for the tasks needed by family (e.g., document editing, form filling, notes and knowledge management), but backed by open models hosted wherever I decide.Then it became richer, now supporting: remote coding agents (wrapping Opencode), delegation and coordination between agents, integration between chat/projects and code agents (this one to me is a pain point for Claude, which I use at work), projects, RAG, rich artifacts support, schedules, and more.You can try it yourself, either cloning the full repo and head to Cerea/kit, or just the deploy kit with a script (https://github.com/paoloviviani/Cerea#quick-start).It's just a docker compose with a ./configure script that helps to generate a sensible .env for the specific scenario. It works in the browser and as a PWA on mobile.I hope you'll enjoy, feel free to reach out for questions. Comments URL: https://news.ycombinator.com/item?id=50384502 Points: 1 # Comments: 0