The Model Context Protocol (MCP) has become a widely adopted standard for connecting AI agents to external services, but its rapid growth has exposed a core challenge: authentication. When agents only answer questions, auth is a simple conversation concern. Once they read emails, update CRMs, write to databases, or call APIs on their own, auth…

For years web authentication has assumed a human behind a browser: click a button, fill a form, verify an email, copy an API key and paste it elsewhere. That model breaks down when the user delegates work to an AI agent. Agents are already writing code, opening pull requests, triaging tickets, querying systems and updating…

StepFun’s StepAudio 2.5 Realtime tackles the core frustrations developers and product teams face when building voice‑driven applications. Real‑time latency often forces a trade‑off between speed and quality, causing noticeable delays that break conversational flow. Many existing voice models still rely on separate pipelines for recognition, reasoning, and synthesis, which adds complexity and points of failure.…

Building reliable LLM applications requires a clear way to store test cases, run consistent experiments, and measure performance without getting lost in ad‑hoc scripts. Teams often struggle with versioning their evaluation data, reproducing runs across environments, and aggregating multiple metrics like accuracy and conciseness in a single view. The result is wasted time debugging mismatched…

Most web agents today operate by taking a single browser action at a time – they receive a screenshot or DOM text, predict the next click, keypress or scroll, and repeat. This step‑by‑step loop made sense when language models had limited reasoning, but now that models can write and debug code, the rigid action‑at‑a‑time design…

Linear attention models compress the unbounded key‑value cache into a fixed‑size recurrent state, which gives constant‑memory decoding but makes editing that compressed memory difficult. In earlier delta‑rule approaches a single scalar step size βₜ controlled both how much old content to erase and how much new content to write. Tying these two decisions together limits…

Many developers and product teams struggle to get reliable, repeatable results from large language models when they are embedded in daily workflows. The core pain points are: having to rewrite the same system instructions for every new task, losing conversation context between runs, and spending time on manual prompt engineering instead of building features. In…

TencentDB Agent Memory solves a core problem for developers building long‑horizon AI agents: as agents run more steps, their context windows fill with verbose tool logs, search results and error traces, causing token bloat and unreliable recall. Traditional memory stacks flatten everything into a vector store, forcing a blind similarity search across disconnected fragments and…

Attackers are now looking beyond production servers and targeting the tools developers keep on their laptops. Packages, editor extensions, browser add‑ons and AI tool configurations sit on developer machines and can be exploited the moment a vulnerability is disclosed. Security teams often struggle to answer a simple question: which developer endpoints are exposed right now?…

Current ways to steer language models either modify whole layers or need heavy extra training. This makes them blunt and can hurt quality. A new neuron‑level method called Contrastive Neuron Attribution (CNA) solves this by finding the tiny set of MLP neurons that separate harmful from benign prompts. You only need a few forward passes,…