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LLMs
71 posts about LLMs. Every summary links to the original.
Compose Multiplatform 1.12.0 Released
JetBrains has announced the release of Compose Multiplatform 1.12.0 with targeted capabilities for artificial intelligence integrations, web rendering, and desktop application layouts. The update incorporates an experimental Model Context Protocol server inside Compose Hot Reload, allowing AI agents to trigger reloads, capture screenshots, inspect the semantic tree, and simulate user interactions directly. For web deployments, the framework now provides automatic font fallback by downloading necessary Noto font subsets on demand when unresolved characters occur during rendering. Desktop developers receive an experimental v2 window and dialog application programming interface in the androidx.compose.ui.window.v2 package, which distinguishes requested states from actual window states and permits precise control over positioning, sizing constraints, and multi-screen placement. These combined enhancements improve developer workflows and runtime flexibility across multiple target platforms.
Elvira MustafinaIdeas Worth a Longer Conversation: The JetBrains Research Podcast
The JetBrains Research Podcast investigates foundational software development and computer science questions through discussions with industry researchers. In team psychology, Cat Hicks demonstrates that collaborative culture and organizational belonging reduce developer anxiety around automated tooling while curbing the risks of overproduction pressure. Historical analysis by Tomáš Petříček shows that programming spans five competing cultures rather than pure specification implementation, explaining why automated code generation historically encounters limits. Regarding artificial intelligence evaluation, Ibragim Badertdinov details how SWE-rebench isolates model capability through dataset decontamination, leveraging the strict verifiability of software test execution for training feedback. Additionally, conversations with Alexander Kulikov and Anna Kogan examine how foundational mathematical understanding aids AI oversight and address adoption hurdles for open-source computer vision infrastructure.
Katie FraserFrom Prediction to Action: How to Turn AI Outputs Into Decisions
Salesforce addressed an operational challenge where sellers faced roughly 12,000 dashboards and over 20 applications outputting machine learning predictions without clear next steps. The engineering team reframed machine learning outputs as raw signals rather than standalone answers. To bridge the gap between assessment and action, they built a Next Best Action layer that combines model signals, business logic, and contextual institutional knowledge into actionable recommendations. They integrated this layer with an AI agent using Model Context Protocol (MCP) tool contracts, enabling dynamic discovery and explicit handling of missing data. Finally, rather than introducing a separate dashboard destination, the agent serves on-demand recommendations directly inside Slack where sellers already collaborate.
Scott NybergGithub ·
Your alt text passes automated checks. That doesn’t mean it’s any good.
Automated accessibility checkers reliably flag missing alt text attributes but frequently miss unhelpful descriptions like raw filenames or repetitive labels. To evaluate image description quality without generating excessive false positives, GitHub built an alt text plugin for the GitHub Accessibility Scanner. The tool combines five deterministic, zero-credential rules that evaluate strings and visual layout spacing with an opt-in vision model check for subjective context. Page context including headings and surrounding prose is extracted alongside images to guide the model using structured outputs and explicit anti-nitpick instructions. While deterministic checks catch unwritten text, the model-driven rule serves as an opt-in prompt for human review rather than an absolute verdict.
Taarik AshenafiHelp AI Coding Agents Write Up-To-Date Code With Modern Golang Skills
AI coding agents often generate outdated Go patterns because older syntax dominates training data and new features fall past model cutoffs. To address this gap, the GoLand team released Modern Go Guidelines, an open-source set of skills and CLI tools covering Go 1.0 through Go 1.27. The tool inspects a project's go.mod file or a specific version flag to supply only the language features and standard library additions compatible with that environment. Using a progressive disclosure approach, the CLI provides concise rule identifiers via a list command and expands into before-and-after examples through an explain command. This mechanism minimizes token consumption while helping agents produce up-to-date, compilable code without modifying project files.
Artem PronichevHow We Optimized the Qwen 3.6 Model for Our Junie Agent
Deploying the Junie coding agent locally on Apple MacBook M5 hardware requires overcoming steep performance bottlenecks during model execution. Engineers optimized the entire stack using Qwen3.6-27B rather than Qwen3.8-27B, which severely degrades without token-heavy reasoning enabled. At the agent layer, Junie appends requests directly into a rolling context and caches initial prompt prefixes to maximize KV-cache reuse across tasks. For model inference, the team disabled reasoning, selected 4-bit quantization, and patched MLX-VLM to execute self-attention prefill matrix operations using faster 8-bit instructions on the M5 processor. Combining these prefill improvements with simultaneous multi-token prediction and n-gram speculative decoding delivered up to a 2x generation speedup.
Stanislav ErokhinJunie Can Now Run Entirely on Your Mac – No Credits, No Cloud
JetBrains has introduced Junie Local, a feature that runs the Junie coding agent entirely on local hardware without cloud dependencies, subscriptions, or credit meters. Users execute a single command inside Junie to download a 4-bit quantized Qwen3.6-27B model, requiring an Apple M5 Mac with 64 GB of RAM. The team prioritized prefill throughput over pure generation speed by utilizing 8-bit arithmetic instructions on the M5 Neural Accelerator, which increased prefill throughput by approximately 40%. Performance optimizations also include KV-cache reuse, speculative decoding to double generation speed, and disabling reasoning to preserve performance. In JetBrains' evaluations, the local configuration performed comparably to cloud-based Sonnet 4.5 on everyday tasks while ensuring source code, diffs, and prompts never leave the local environment.
Dmitry SavelevSay it once: introducing Bot Preference Sync
Cloudflare announced Bot Preference Sync, a feature across all customer tiers that automatically reflects dashboard AI bot configurations directly in a site's robots.txt file. Website operators previously faced challenges synchronizing static robots.txt directives with edge enforcement rules, which led some crawlers to ignore stated preferences or attempt bypasses. Under this system, Cloudflare generates and prepends directives for Search, Agent, and Training categories to existing robots.txt files using crawler lists tracked in BotBase. Mixed-use crawlers performing both search indexing and training must meet strict transparency criteria, including honoring opt-outs and providing page-level visibility, to avoid edge blocks when training is disallowed. Additionally, onboarding workflows allow ad-supported publishers to automatically set AI model training to Disallow by default while retaining search visibility.
Jin-Hee LeeGrab ·
Building Jarvis Pro: Route first, answer later
Grab designed Jarvis Pro as an AI assistant for account managers turning complex account data into actionable merchant conversations. Early prototypes generated polished but operationally unsound advice, such as pushing promotions when order drops were caused by outlet pauses and fulfillment issues. To address this, the team implemented a "route first, answer later" design that uses a prompted LLM classification step constrained by a routing guide to dictate context, tool paths, answer structure, and guardrails before generation. The pipeline also isolates narrow memory decisions, reconciles metric freshness across data sources, and caps tool calls to maintain usability. In offline evaluations across 351 realistic prompts, routing matched safe routes 99.4% of the time while answer quality scores in a portfolio subset improved from 78.5 to 91.0.
Christian CoffrantWhy AI-Generated Code Is Easy but Engineering Trust Is Hard
At Salesforce, engineering teams discovered that AI coding agents could produce internally consistent code and passing test suites that still failed to meet actual requirements. To ensure correctness before writing code, the team developed an agentic Spec-Driven Development workflow centered on explicit specifications and gated verification. The system separates evidence-based repository lookups handled by agents from subjective judgment calls escalated to human engineers. Implementation plans must cite repository evidence reviewed by a Skeptic Agent, while a Compliance Matrix traces each success criterion directly to executable test proof. Finally, a multi-agent review system evaluates the implementation under an asymmetric rule allowing an independent judge to downgrade passing checks but never override failed gates.
Scott NybergPyCharm for AI-assisted Django Workflows
According to the 2026 Django Developers Survey, ninety percent of respondents incorporate artificial intelligence into their weekly or daily workflows. While agents generate code rapidly, developers remain accountable for understanding, evaluating, and shipping changes within their applications. PyCharm addresses these demands by integrating support for external agents, local models via Ollama and LM Studio, and customizable agent skills across codebases. The IDE maintains version-specific framework assistance, including auto-completion for Django 6.0 template partials, alongside architectural inspection tools and visual diffs. Furthermore, developers can trace application components, inspect API endpoints, execute HTTP requests, and query database migrations directly through built-in tooling.
Will VincentHow to Evaluate Production AI Agents: Measure System Outcomes, Not Conversations
Evaluating AI agents solely on conversational transcripts fails when agents must invoke tools to modify external production systems. Language models can convincingly narrate that an action occurred while completely omitting the underlying tool call, leaving database or billing records unchanged. Outcome-based evaluation addresses this divergence by treating external system mutations rather than generated dialogue as the actual source of truth. Frameworks such as CRMAgentBench implement persistent, stateful environments with strict all-or-nothing validation, verifying tool arguments, execution sequence, final record modifications, and the total absence of unauthorized actions. Furthermore, measuring repeated success across independent executions using pass^k metrics exposes operational variance and inconsistency that standard single-shot scores and pass@k metrics conceal.
Scott NybergHow Cloudflare detects MCP traffic and helps secure it
Traditional security boundaries assume human speed and judgment, but AI agents execute nondeterministic actions rapidly and can invoke unapproved Model Context Protocol servers through standard HTTPS traffic. To mitigate this shadow traffic, Cloudflare One introduces detection and inspection capabilities across clients, network boundaries, and MCP servers. Cloudflare Gateway leverages protocol-level headers and JSON-RPC inspection to identify direct MCP connections, verify managed paths, and enforce access through approved MCP Server Portals. Furthermore, Cloudflare Agents SDK v0.20.0 implements the stateless 2026-07-28 MCP specification while providing fallback handshakes for legacy servers. Organizations can combine Gateway traffic source rules with server middleware like WriteGuard to block unauthorized tool invocations before execution.
AJ GerstenhaberHow Agentforce-Powered AI Security Workflows Accelerate Incident Response
Salesforce engineers expanded Security Center from a basic conversational interface into an Agentforce-powered stateful investigations platform for incident response. Managing enterprise security incidents required handling long-running lifecycles, disparate telemetry formats, and finite LLM context windows that risk reasoning pocket collapse. To prevent overflowing context limits, the architecture partitions telemetry into contextual segments and uses AI-driven summarization pipelines before feeding data to the agent. Because non-deterministic LLM outputs break traditional deterministic testing, the team implemented AI-driven evaluation pipelines using simulated customer interactions to evaluate response behavior rather than exact wording. Ongoing work focuses on grounding public foundation models with curated, Salesforce-owned security knowledge to improve platform-specific remediation guidance.
Scott NybergGrab ·
Grab Bench: Evaluating AI on Grab-shaped production work
AI models evaluated on internal tasks often produce subtly plausible errors, such as altering metric definitions in SQL queries or missing hidden stateful invariants in code patches. To address this, Grab built Grab Bench, a configurable evaluation harness that executes model providers through specialized task plugins. The platform evaluates synthetic or redacted datasets that preserve production constraints across domains like query generation, tool use, passenger-profile reasoning, and agentic coding. By recording granular row-level outputs with deterministic scorers and LLM judges, the harness exposes failure modes rather than relying solely on aggregate leaderboards.
Christian CoffrantGithub ·
Using the GitHub Copilot SDK for Java
The GitHub Copilot SDK for Java offers a framework-agnostic client library to orchestrate AI agent sessions and tool execution directly from server-side Java code. Unlike framework-dependent alternatives, the SDK supports direct model providers such as OpenAI, Azure, and Anthropic through custom endpoint configurations without requiring a Copilot subscription. Developers can register tools declaratively using the experimental @CopilotTool annotation processor or dynamically through inline lambda definitions with ToolDefinition.from. When integrated into a Jakarta EE 11 application on Open Liberty, agent workflows run on container-managed virtual threads that propagate CDI and transaction contexts during blocking calls like sendAndWait. Real-time event subscriptions capture model execution steps and tool invocations to stream updates over WebSockets without exhausting platform threads.
Edward BurnsEverything we launched during Agents Week
Agents Week introduced a series of products and primitives across Cloudflare to support an Agentic Internet where autonomous software and humans interact. Developing capable agents requires addressing technical challenges spanning identity, communication, orchestration, memory, observability, and security. Cloudflare introduced the Agent Development Lifecycle alongside tools like Cloudflare Agents for live tracing, replay, and human-in-the-loop approvals. The releases also included programmable CI/CD pipelines capable of repairing failures, Cloudflare Wallets for agent transactions, and Kitesurf, an agent-first browser running in V8 isolates. New protocols and tools like WebMCP, MCPv2, and Cloudflare AI Search aim to make web data and applications readable, discoverable, and searchable by autonomous software.
Shelley JonesUnveiling good and bad behaviors on the Agentic Internet
Web traffic increasingly shifts between human interaction and automated agents, rendering point-in-time security checks ineffective against sophisticated automation. Cloudflare addresses this shift through continuous behavioral analysis designed to evaluate trust across entire user sessions rather than relying on static point-in-time hurdles. Its CDN-injected client-side system, Precursor, tracks continuous behavioral indicators like cursor movements to catch mid-session automated takeovers. To counter rapid bot adaptation, Cloudflare is introducing self-adjusting machine learning models under Adaptive Intelligence and non-deterministic defenses such as the AI Labyrinth. These tools allow site owners to poison scraper training data, trap crawlers in synthetic webs, or queue legitimate agents while dynamically penalizing abusive traffic.
Jin-Hee LeeIntroducing Radar Researcher: An AI tool for exploring Internet data in plain language
Cloudflare launched the beta of Radar Researcher, an AI tool designed to let users explore global Internet traffic data using plain-language queries. Built entirely on Cloudflare's developer platform, the system uses the Cloudflare Agents SDK within a Cloudflare Worker, maintaining conversation state and SQLite databases inside Durable Objects. The inference layer relies on Workers AI running models like Kimi K2.7 through an ordered fallback chain across three model families to prevent outages, routed via AI Gateway for logging, caching, and guardrails. Radar's frontend renders retrieved data using its existing native interactive charts, while smaller dedicated models generate conversation titles and follow-up suggestions asynchronously. Additionally, Radar introduced support for the WebMCP standard, exposing imperative and declarative tools to allow external browser agents to navigate and interact with the platform directly.
Lai Yi OhlsenUnifying Workers AI and AI Gateway into a single AI control plane
Workers AI and AI Gateway are converging into a unified AI control plane to simplify model access, billing, and observability. Developers can now access both services through a single entrypoint using unified Workers bindings and the /ai/ REST endpoint. Specifying a default gateway automatically instruments requests with payload logging, token tracking, and cost attribution without manual dashboard setup. Cloudflare has also rolled out unified prepaid billing, allowing teams to spend AI Gateway credits on Workers AI models while unlocking higher rate limits. Planned future features include model-first routing for automated provider failover and smart routing powered by task classifiers on Workers AI.
Michelle Chen