LLM Prompt Caching Versus Semantic Caching Caching LLM costs is two different problems: reuse the prefix with prompt caching, reuse the answer with semantic caching, and never confuse one for the other https://t.co/vuFGgKiIvJ
#Tutorials / practice#RAG#English text is being prepared.
there's something really cool about asking your agent to draw a statechart (hypothetical example in this image), then using the /create-formal-verification-skill you made with pstack to find bugs through something like TLA+. you can both understand your code better and how the user flow should work, and fix issues users would otherwise see
#Tutorials / practice#Agents#Open-source ecosystem
The Google developer blog published an Agent evaluation path that moves step by step from key user journeys, simulated users, and manual Trace review to automated evaluation and merge gates. The article points out that an Agent may fall into loops, repeatedly call tools, and consume large amounts of Tokens, yet still ultimately give a fluent answer, so asserting only on the final output is not enough to prove it can go live. The article breaks the path into Day 0: manually reading Traces of 10 to 20 high-risk chains; Day 1: four layers consisting of unit tests and deterministic metrics, LLM Judge, and integration tests; and Day 2: two gates, an overall baseline and zero regression on core risk items, and gives a minimal assertion example based on pytest.
#Tutorials / practice#Agents#Evaluations / benchmarks
Playwright CLI uses snapshot to obtain the page's accessibility tree and mark interactive elements as referenceable nodes, then uses find to perform local retrieval by text or regular expression, and finally uses element references to perform operations such as fill and click. Using a login scenario, the article compares three approaches: CSS selectors, semantic locators, and CLI snapshot retrieval, and points out that find is snapshot text or regular-expression matching rather than vector retrieval. The author also reminds readers that element reference numbers become invalid as the page changes, and that formal tests still require maintainable locators and assertions to verify results.
#Tutorials / practice#Agents#English text is being prepared.
Aerial is a tool that can replace the default launcher on Chinese smart TVs without root. It uses a "launcher guard" to overlay the original launcher when it is woken up, and leverages Android notification listening to revive within 0.5 seconds after standby cleanup, so home-screen ads disappear, though boot ads still cannot be blocked. During deployment, connect the TV and computer to the same WiFi, enable ADB and find the TV's IP, then copy the prompt from the Aerial official website and hand it to Agents such as Doubao and WorkBuddy to execute; the user only needs to allow USB debugging in the TV pop-up, and the whole process takes about five minutes. The tool—from code and APK to the official website—was produced entirely through AI Vibe Coding, gained over 200 stars within one day of launching on GitHub, and subsequently added features such as icon hiding and adding signal input sources to the home page, along with adaptation for multiple TV models.
#Tutorials / practice#Agents#Open-source ecosystem
Following the alert lifecycle, the article breaks AIOps into four layers—detection, convergence, diagnosis, and remediation—and discusses the engineering implementation approaches and known boundaries of each layer. The detection layer replaces fixed thresholds with dynamic baselines; the convergence layer reduces noise through five mechanisms: deduplication, correlation aggregation, debouncing, time suppression, and dependency suppression. When topology data is complete, achieving overall noise reduction of over 70% is a common level. The diagnosis layer emphasizes unified modeling and multi-agent collaboration, outputting root cause candidates plus evidence chains rather than definitive judgments; when evidence is insufficient, it conservatively provides Top-K directions and flags missing evidence.
#Tutorials / practice#Agents#English text is being prepared.
Browser Use has released its 2026 roundup of browser Agent MCP servers, dividing such servers into two modes—driver and delegate—and evaluating four of them one by one: Browser Use Cloud MCP, Browser Use open-source MCP, Hyperbrowser MCP, and agent-browser chat. In a comparative evaluation across 100 BU Bench V2.1 tasks, the author found that the four driver-type tools consumed a median of 2.1M to 6.7M input tokens per task, all of which count toward the coding Agent's context. The article also provides each server's installation commands, model selection, billing methods, and known limitations, and explains that long tasks, parallel tasks, and structured-output scenarios are better suited to the delegate mode.
#Tutorials / practice#English text is being prepared.#Agents
English text is being prepared.
#English text is being prepared.#Agents#Video
#Evaluations / benchmarks#Search#DeepSeek
English text is being prepared.
#Tutorials / practice#Agents#English text is being prepared.
I just published my roadmap for building evals on highly subjective tasks. Here are the key steps involved and practical tricks for reducing noise / increasing confidence… Blog is here: https://t.co/uo0FsWtkYF Why is this difficult? Evaluating agents is already hard due to their broad scope, emphasis on expensive / long-horizon tasks, and non-determinism. Subjective tasks make this even harder because we cannot deterministically verify solutions and must rely on human judgments (or LLM judges). This introduces additional noise into an already difficult evaluation process. Evaluation roadmap. We can build reliable evaluations using a repeatable framework with three components: - Human evaluation: Our North Star for quality. Create quality guidelines that clearly define quality with the help of subject-matter experts, calibrate human evaluators to ensure agreement, and continually monitor evaluation quality. - Offline evaluation: Use calibrated LLM judges or task-specific rubrics as an efficient proxy for human evaluation. This allows us to scale experimentation without relying on expensive human evaluation for every new model. - Production monitoring: Track quality signals from real-world agent usage and use A/B testing to validate improvements before fully deploying a new agent. Multi-stage evaluation. These components form a tiered evaluation process: Offline evaluation → Human evaluation → Production A/B test → Full deployment We can test many ideas automatically, send promising candidates to human evaluation, and validate improvements through production A/B testing. Continual production monitoring then allows us to discover new use cases / failure cases in production that we can add into the earlier stages of evaluation to create a feedback loop. Sampling useful data. Evaluation quality depends heavily on which tasks we select. We want tasks that are diverse, realistic, difficult, discriminative, and accurate. Techniques like structured task taxonomies, model-based difficulty filtering, Item Response Theory (IRT), and LLM-based auditing help us construct more informative evaluation sets. Handling noise. We should leverage statistics throughout the evaluation process: - Power analysis: Estimate the number of evaluation samples needed to detect meaningful improvements. - Confidence intervals: Quantify uncertainty and provide clear confidence signals alongside evaluation results. - Re-sampling: Repeat agent / judge evaluations to reduce within-task variance and potentially tighten confidence intervals. - Paired comparisons: Evaluate models on the same tasks to more reliably detect performance differences. Key takeaway. An improved benchmark score does not always mean our agent is more capable. We need well-designed evaluation tasks, calibrated human / model evaluators, and statistical confidence signals to distinguish meaningful capability improvements from noise (especially on subjective tasks). By combining these techniques into a repeatable evaluation process / roadmap, we can make building and maintaining evaluation suites more systematic, reliable, and scalable.
#Tutorials / practice#Evaluations / benchmarks#Agents
When our AI code reviewer's cost per run doubled, we dug into the token usage to find out why. Learn how we used prompt caching and intelligent model routing to slash our inference bill by 68% while speeding up reviews. https://t.co/FhCVDuy9z5
#Tutorials / practice#English text is being prepared.#English text is being prepared.
#Evaluations / benchmarks#Anthropic
#Evaluations / benchmarks#Speech
SuperEDA2 is an MCP server that lets Claude Code design KiCad 10 schematics and circuit boards, and writes the reason for each design decision directly into component, net, and copper layer fields. The author gave only this one instruction, without specifying part numbers, circuit diagrams, or component counts, and the final design generated 281 components, 6 layers, 100×100 mm, 2054 traces, and 434 through-holes, with 0 schematic ERC errors and 0 PCB DRC errors, unconnected items, and schematic mismatches. The board carries four AD9254BCPZ-150 (14-bit/150 MSPS), ADA4932-1 differential amplifiers, CDCLVD1204 clock distribution, and a buck circuit plus six LDOs. This article was compiled from hackster.io.
#Tutorials / practice#English text is being prepared.#English text is being prepared.
The AWS Solutions Architecture team built and open-sourced the multi-agent system Deep Insight, which can transform CSV data and natural language prompts into cited .docx analysis reports. The system consists of eight agents—Coordinator, Planner, Supervisor, Coder, Validator, Reporter, Tracker, and others—collaborating in layers, controlling execution flow based on the Graph and Agents-as-Tools patterns of the Strands Agents SDK, and selecting Claude Haiku, Opus, and Sonnet by role. Production deployment relies on the Amazon Bedrock AgentCore runtime; LLM-generated code is executed in isolation within per-session ephemeral containers on AWS Fargate, accompanied by a Web UI and an Ops dashboard.
#Tutorials / practice#Agents#English text is being prepared.
Fireworks AI announced that Muse Glimmer 30B can be trained with SFT for free until November 30, and released a complete recipe for a prediction experiment using historical Kalshi market data. The fine-tuned Glimmer achieved 58.3% accuracy on unseen topics, 11.2 percentage points higher than Jev's 47.1%; in a historical backtest, a hypothetical $1,000 grew to $1,071 after accounting for simulated fees, while both base Glimmer and Jev lost money. Training used LoRA adapters and a K-way cross-entropy loss, ran through the training data only once, and separately used a calibration set to fit a temperature parameter for each question category.
#Product updates#English text is being prepared.#Open-source ecosystem
Zhidongxi selected 12 noteworthy DeepSeek Harness third-party plugins and compatible projects from platforms including GitHub, X, and Reddit, taking into account GitHub Star counts and community attention. Among them, the main repository of the open-source design tool OpenDesign has reached 102,800 Stars, the sidebar enhancement plugin DSH Better Sidebar has about 4,400 Stars, and the context management plugin dsh-context has about 2,700 Stars; the latter two were followed and recommended by Cui Tianyi, the head of the DeepSeek Harness team. The article also notes that as plugins increase, host version upgrades may cause plugins to fail, and that plugins that read local files or execute commands involve permission and security issues.
#English text is being prepared.#Agents#English text is being prepared.
OpenAI models helped researchers—operating as part of our Patch the Planet initiative—discover critical security flaws in PHP, an open-source programming language used to run an estimated 70% of websites. Over the past two months, we led a coalition of maintainers, researchers, and security providers to prepare fixes and protections ahead of disclosure. This duration was necessary because the fix was quite intricate and required a major change to the PHP engine. Every site using PHP can now safely upgrade their version to stay secure, in addition to applying mitigations from various security vendors. Due to the severity of these flaws and PHP’s widespread use, we consider this bug to be one of the most impactful vulnerabilities in recent memory. It also marks a new phase, where AI can find security bugs with internet-scale impact. This is an example of the Defender’s Window in action: to our knowledge, frontier AI was able to find this vulnerability ahead of attackers, giving defenders time to prepare. https://t.co/TW3jrSbB3f
#Product updates#English text is being prepared.#Agents
Talking at 3pm at @nvidia GTC Berlin on how @Shopify post-trains open source models and optimizes inference using Nvidia's models and tools. Read our blog post about it here: https://t.co/bkKnkzhCCg
#Tutorials / practice#Agents#English text is being prepared.