标签：#tex

Forget email surveys or long calls spent on hold. Voicebox lets people send customer feedback by recording a voice note on their phone.

If you are an active user of AI-powered IDEs like Cursor, VS Code with Copilot, or Windsurf, you already know the sinking feeling of seeing this notification: "You have used 100% of your fast premium requests for this billing cycle." Suddenly, your snappy, context-aware coding assistant slows to a crawl or starts racking up expensive pay-as-you-go API bills. At the same time, you are likely paying $20/month for a ChatGPT Plus or Team subscription that sits underutilized in a browser tab. You use it for general questions, but it lacks direct, real-time access to your local codebase, forcing you to engage in a tedious dance of copying and pasting code blocks. What if you could bridge this gap? What if you could let ChatGPT Web do the heavy reasoning and planning using your local context, while saving your premium IDE tokens for fast auto-completions ? In this article, we’ll explore a highly novel, intermediate-level setup that does exactly this. By leveraging the Model Context Protocol (MCP) , Node.js , and secure Cloudflare Tunnels , you can route heavy code-planning tasks directly to your web-based ChatGPT Plus subscription safely and completely free of extra token charges. The Philosophy: Let ChatGPT Think, Let Your IDE Work When building complex software with AI, your workflow generally splits into two distinct phases: Reasoning & Planning (High Token Usage): This is where you ask the AI to read 10 source files, understand the architecture, design a new feature, or find a subtle bug. This consumes massive amounts of context window tokens. Execution & Autocomplete (Low Latency): This is where the AI writes single lines of code, refactors a function, or autocompletes your imports. This requires fast, inline API queries. Paying premium API rates (per token) for Phase 1 is incredibly expensive. This is where this open-source MCP bridge project shines. It exposes a read-only view of your local project as an MCP server. Your web-based ChatGPT (via custom GPTs or MCP int

Microsoft Execution Containers provide a cross-platform framework for isolating AI agents within secure sandboxes to protect private data and system integrity. This technology allows developers to manage the lifecycle of autonomous code while ensuring that unpredictable agentic workflows do not access sensitive local files or unauthorized network resources. The Evolution of Agent Security and Isolation Trust remains a significant hurdle for developers building modern AI agents, particularly those operating on edge systems. When agents combine local processing with cloud-based intelligence, they often require access to sensitive information to be effective. However, granting this access creates a risk that the agent might call unintended APIs or compromise private user data. Historical attempts to launch autonomous agents in the 1990s largely failed because of these security concerns. Delivering arbitrary code to local machines proved too risky for mainstream adoption. Today, hardware-assisted virtualization has changed the landscape. This technology serves as the foundation for modern security models, including isolated operating system components and cross-platform tools like the Windows Subsystem for Linux. Microsoft now utilizes these virtualization advancements to build a more reliable framework for agent operations. By running agents in secure containers or microVMs, the system separates their activities from the primary operating system. This isolation ensures that even if an agent receives a poorly constructed prompt, it cannot delete critical system files or leak sensitive information. Managing Developer Environments Developers need a way to build code in flexible environments while still planning for restricted production deployments. Microsoft Execution Containers (MXC) address this by offering a policy-based restriction model. This framework allows for the creation of managed, isolated containers that follow specific security protocols. Applying Policy-Ba

Fabiane Nardon shares how TOTVS prepares enterprise data for token-hungry AI agents. She discusses balancing deterministic logic and non-deterministic LLMs across precision, security, and cost. Nardon details using data mesh, low-latency database architectures, semantic ontologies, and dynamic MCP tool selection to optimize context windows and reduce token overhead in transactional systems. By Fabiane Nardon

Archeologists are confronting an “unfathomable” loss as border wall construction is set to cut across ranches that are home to unique historical sites, many of which haven’t been fully studied.

We built a plugin for the GitHub Accessibility Scanner to make sure your alt text is actually accessible. Here's how it works. The post Your alt text passes automated checks. That doesn’t mean it’s any good. appeared first on The GitHub Blog .

Cloudflare is introducing WriteGuard, now in private beta, to provide fine-grained security controls for MCP (Model Context Protocol) servers. It aims to make AI agents safer by controlling their access to tools that can modify data or perform actions, rather than simply read information. By Sergio De Simone

Grafana Labs has announced general availability for two tools that let AI coding agents query live observability data during development: the gcx CLI and the Grafana MCP server. Both allow agents to pull metrics, logs, traces, SLOs, and Synthetic Monitoring results from Grafana Cloud or a self-hosted stack By Claudio Masolo

Short answer: for a small fintech product that turns reviewer voice notes into structured code findings, start with one synchronous speech-to-text file-upload adapter for MP3 and WAV, but write every upload to a tenant ledger before making the transcription request. That is usually the fastest integration because it keeps the first release small while preserving per-tenant cost visibility and a clean path to regional routing. Choice Shipping effort Tenant attribution Best fit Main constraint Direct file upload Lowest Clear with an internal ledger Short reviewer notes Bound by the selected API's request and duration limits Object storage plus async worker Medium Clear with job records Long or bursty recordings More states to operate Self-hosted transcription Highest Fully internal Strict control requirements or sustained workloads Model serving becomes your job My recommendation is the first row for the initial release. Keep the adapter replaceable, measure billed units rather than guessing from file size, and promote work to a queue only after real upload patterns justify it. The point isn't to find a universally fastest model. It is to ship weekly without losing the tenant-level evidence needed to understand margin. How should a simple speech-to-text API handle MP3 and WAV file uploads? Treat the upload as a business event, not as an anonymous call to an AI endpoint. Before sending any audio, create an internal record with tenantId , changeId , uploadId , media type, byte count, selected processing region, and a start timestamp. After transcription, add the external request identifier when one exists, the terminal status, and the billable unit reported by the selected service. A byte count is useful for capacity planning; it is not a substitute for actual billing data. That distinction matters in a multi-tenant SaaS. One tenant may submit many short WAV notes, while another submits compressed MP3 files with longer conversations. Charging, margin analysis, and abuse

The MCP 2026-07-28 specification removes the initialize handshake and session header, and adds required method and tool-name headers so gateways can route agent traffic without parsing JSON. Reaction split between developers calling it a rediscovery of REST and those arguing the standard itself was always the point. By Steef-Jan Wiggers

Originally published on Loop & Retry — field notes on building LLM agents that survive production. Here's the failure mode that surprises people who've only reasoned about agents statistically. You measure a per-step error rate — say 10% of steps produce something wrong — and you assume errors are independent, so a wrong step is a wrong step and the rest of the run is fine. Then you watch a real trajectory and see something else: step 4 gets a fact slightly wrong, step 5 reasons on top of that wrong fact and commits harder, step 6 takes an action premised on both, and by step 8 the agent is confidently executing a plan that was doomed at step 4. One mistake became five. The errors weren't independent — they were coupled through the context , and coupling is what turns a 10% step-error rate into a run that's wrong far more than 10% of the time. This is the cascade : a single fault amplifying down a single trajectory. It's distinct from the failure I wrote about in distributed retry patterns , where the problem is one bad condition hitting many workers at once — that's a blast radius, a horizontal spread. The cascade is vertical: it spreads through time within one run, because an agent's own past output is its future input. This post is about the vertical kind, why it's structural rather than bad luck, and where you can cut it. Why coupling is the default, not the exception A stateless function that fails just returns an error. An agent that fails does something worse: it writes the failure down where it can read it again. The mechanism is the same one that makes agents work at all — the transcript accumulates, and every step conditions on everything before it. That's a feature for carrying intent forward. It's also the exact channel a mistake travels down. Three ways a single fault propagates through the context: Poisoned premise. The agent derives or retrieves a wrong fact — a misparsed tool result, a hallucinated ID, a stale value — and it lands in the transcript a

Instacart introduced Blueberry, an AI-assisted incident response system that helps on-call engineers investigate production issues faster. It combines AI agents, operational data, and historical incident knowledge to generate grounded root cause hypotheses in Slack. It uses parallel subagents, MCP integrations, and incident history to reduce investigation time while keeping engineers in control. By Leela Kumili

Microsoft released a dedicated AI Gateway tier of Azure API Management in public preview, with a control plane built around models, MCP servers and tools rather than APIs. It fronts Foundry, Bedrock, Vertex AI and OpenAI behind one endpoint, with policy cards instead of XML. Architects welcomed the consolidation while questioning where the governance boundary sits. By Steef-Jan Wiggers

LaTeX is a document preparation system for high-quality typesetting, perfect for academic papers and technical docs. Many people turn to Overleaf as their go-to online editor for LaTeX, but it comes with its own frustrations. If you are tired of Overleaf being costly and always hitting the compile timed out error, this guide is for you! The full MacTeX install weighs in at a massive ~6.4GB, most of which you'll never actually use. Setting up a minimalist LaTeX environment on macOS using BasicTeX and VSCode is a much better alternative that makes your setup ~8 times smaller. It saves storage and makes it much easier to collaborate with your teammates using GitHub as a combo. Install LaTeX via Homebrew We'll use Homebrew to keep things manageable. If you don't have it, grab it at brew.sh . 1. Install LaTeX BasicTeX is the "lean" version of MacTeX. It's only ~140MB initially. brew install --cask basictex 2. Refresh your path and verify Make the TeX binaries available in your current terminal session: eval " $( /usr/libexec/path_helper ) " The default LaTeX compiler pdflatex should be available now. Verify it's working: which pdflatex pdflatex --version 3. Update tlmgr and packages tlmgr is the TeX Live Manager. To update tlmgr and all packages, run the following commands: sudo tlmgr update --self sudo tlmgr update --all 4. Install latexmk (build manager) latexmk is the "build manager" that handles multiple runs of the compiler (necessary for bibliographies and tables of contents). sudo tlmgr install latexmk Verify latexmk version: which latexmk latexmk --version 5. Install essential package collections BasicTeX is too bare-bones for real projects. Since we went minimalist, we need to grab only the packages we actually use. These three collections will cover 90% of your needs while keeping storage down. sudo tlmgr install collection-latexrecommended sudo tlmgr install collection-fontsrecommended sudo tlmgr install collection-latexextra Note: If a build fails due to a mi

Texas Governor Greg Abbott has paused new data center development until an audit has been completed.

This article presents a defense-in-depth approach for securing Model Context Protocol (MCP) deployments in production. It outlines four architectural control layers: safe execution, management infrastructure, outbound trust, and semantic integrity, arguing that production security requires enforcement beyond the gateway at the earliest trustworthy control points. By Nik Kale

Since launching last year, the startup today has more than 8 million people using the open-source or hosted version of its models, and now generates annual recurring revenue of $21 million.

My CV is a mess, but I think almost everybody got the same result over time. Lot of experiences, certifications, projects and so on. Furthermore, having his CV in LaTex is great but it can be hard to update/upgrade it when needed... Why not using something like a database to store all those elements? Well, we can use another layer or tool to deal with that, but it seems LaTex can do that as well with the pfgkeys package. The idea here is to create a really small local package offering an interface to store different kind of elements per categories. Let start with our requirement. How to put experiences? \experiencePut { devto }{ date }{ 2026 } \experiencePut { devto }{ title }{ author } \experiencePut { devto }{ summary }{ Writing article for fun and no profit } \experiencePut { devto }{ location }{ somewhere on the web } \experiencePut { devto }{ company }{ dev.to } \experiencePut { devto }{ skills }{ tex, latex, erlang, elixir, dart, flutter } \experiencePut is taking 3 arguments, the first one will be a reference to a position (e.g. devto ), the second argument will be an unique keyword (e.g. date ) and finally, the last argument will be the data to store (e.g. 2026 ). How to get this information back? \experienceGet { devto }{ date } \experienceGet { devto }{ title } \experienceGet { devto }{ summary } By creating experienceGet , where the first argument is the reference to a position (e.g. devto ) and the second one to the unique keyword previously set (e.g. date ). Then, it will return 2026 . Neat. Right? Let create our local package called map.sty . $ touch map.sty It will contain the definition of our interfaces and few mandatory elements for LaTex. Thanks to overleaf, we have now great LaTex documentation about that. \NeedsTeXFormat { LaTeX2e } \ProvidesPackage { mystore } [2026/07/24 map package] \RequirePackage { pgfkeys } pfgkeys requires a list of keys to work correctly, then the /cv/ key is created. More information can be seen on the documentation reg

Age verification is okay, but filtering is preempted by Section 230, judges find.

Traditional API gateways assume deterministic services and simple schemas - assumptions agentic AI breaks. Discover why enterprise engineering leaders are adopting AI Gateways as an evolutionary architecture seam. Centralize guardrails, model routing, agent identity, action policy, and semantic audit within a single control plane to prevent costly incidents while keeping core platforms stable. By Joe Price, Branimir Đurek, Pavlos Migkiros, Trevor Dearham