标签：#tools

Scheduling and calendar sync, all in one Discussion | Link

Connect Your AI Spend Directly to Your Roadmap Discussion | Link

OpenAI is expanding ChatGPT beyond chat with ChatGPT Work , a cross-platform work environment that includes a desktop agent able to interact with local applications, files and browser content. The change matters because it moves ChatGPT closer to an operational role: not only explaining how to complete a task, but potentially clicking, typing, moving files and staying engaged with a project over time. In OpenAI's official announcement on ChatGPT Work , the company describes a unified experience across web, mobile and desktop. The desktop app combines Chat, Work and Codex, while its built-in browser and local computer capabilities are intended to support more contextual, end-to-end work. OpenAI's terminology centers on ChatGPT Work and Computer Use. "Computer History," the name used in the originating signal, is not the feature name used in the official announcement. The underlying shift is significant for developers and knowledge workers. A chat interface has traditionally depended on users copying information into a prompt, describing where files live, and manually carrying results into the next application. Desktop automation can reduce those handoffs, provided users grant the relevant access and organizations establish appropriate controls. From answers to work across a computer ChatGPT Work is positioned as an agentic layer for work that spans apps and files. OpenAI says the desktop agent can act locally in the background, including interacting with applications, files and browser content. It also highlights plugins, workflows and Scheduled Tasks as ways to connect tools and automate recurring actions across connected apps and local files. That does not mean every task should be delegated without review. The practical value depends on how clearly a workflow can be defined, the permissions it requires, and the consequences of an incorrect action. For example, moving or modifying local files is fundamentally different from drafting a response in a chat window. The

Browser-based generative DAW Discussion | Link

The keyboard-first calendar to get organized quickly Discussion | Link

OpenAI is expanding its inference infrastructure through a multi-year partnership with Cerebras, aiming to support faster responses for real-time AI workloads. The centerpiece is GPT-5.6 Sol Ultrafast , a Cerebras-backed deployment that OpenAI says can reach up to 750 tokens per second during a limited preview. For enterprises, the development is less about a minor model setting and more about whether frontier-model intelligence can be used in workflows where latency materially affects the experience or business process. OpenAI's official Cerebras partnership announcement confirms plans for 750 megawatts of ultra-low-latency AI inference capacity for OpenAI customers. The capacity is scheduled to come online in multiple tranches through 2028, making the agreement a long-term infrastructure expansion rather than a one-off model launch. What the OpenAI and Cerebras partnership changes OpenAI is adding Cerebras wafer-scale compute to its inference stack. The stated objective is to provide faster responses and enable real-time AI experiences across customer workloads. Cerebras has separately identified GPT-5.6 Sol as the model used for the Ultrafast deployment, positioning the offering around high-speed access to OpenAI's flagship GPT-5.6 family model. The relevant distinction is between building a more capable model and serving an existing frontier model with a lower-latency compute path. OpenAI's announcement is focused on the latter. Cerebras hardware is being deployed to accelerate inference, the stage at which a trained model processes prompts and generates responses for users or applications. That focus matters for enterprise systems where delay can compound across a workflow. A faster model response can improve the feel of interactive tools, but it can also shorten multi-step agentic processes , reduce waiting in human review loops, and make real-time assistance more practical. The announcements do not specify which individual business applications will receive a

When mapping digital footprints, security analysts and open-source intelligence (OSINT) practitioners rely heavily on registration checkers. For years, single-purpose utilities like Holehe were the industry standard for checking email recovery endpoints. However, modern target profiling requires deeper correlation, higher concurrency, and cross-platform pivoting across both emails and usernames. Enter user-scanner —a high-throughput, 2-in-1 Python OSINT engine designed for deep email registration checking, username profiling, and cross-scan intelligence. Technical Comparison: user-scanner vs. Legacy OSINT Tools Feature / Capability Holehe Sherlock / Maigret user-scanner Primary Input Vectors Email Only Username Only 2-in-1 (380+ Combined Vectors) Target Integration ~120 Email Sites Scrapes Web Forms 155+ Email & 225+ Username Sites Pivoting / Cross-Scanning ❌ No ❌ No ✅ Auto-Pivots (Email ↔ Username ↔ Links) Breach Intelligence ❌ No ❌ No ✅ Hudson Rock Infostealer API ( --hudson ) Engine Core Basic Async Basic Requests httpx + curl_cffi (TLS Impersonation) Reporting Formats CLI / JSON CLI / CSV / HTML PDF (with Media/Avatars), JSON, CSV Deployment / Ecosystem Pip Pip Pip, Virtual Env, Nix ( nix run ) Core Capabilities of user-scanner 1. Cross-Scan & Pivot Intelligence Engine Unlike legacy checkers that stop after returning a boolean hit, user-scanner features an automated cross-scanning engine ( --cross-scan ). It mines exposed handles, profile links, and secondary email addresses from initial scan metadata and recursively pivots across secondary target vectors. -e → Username: Extracts handles or social links exposed on an email's registered profile. -u → Email: Extracts public email addresses published on target profile pages. Multi-Depth Chains: Supports configurable chain depth ( --cross-depth ) and link validation rules ( --cross-links verified ). 2. Infostealer Breach Intelligence ( --hudson ) Integrates directly with Hudson Rock's infostealer malware infection l

Google's smartest workhorse yet for coding & agents Discussion | Link

Free coding agents to kill Claude, Cursor, Replit, and Devin Discussion | Link

Multitrack synth, sampler, drum machine, and looper for iOS. Discussion | Link

Connect 2,500+ European banks to track your spending Discussion | Link

know which of your lines a vendor just broke Discussion | Link

Personalized Nervous System Training Discussion | Link

I fixed this script's frontmatter parser a week ago. A draft with an unclosed --- block used to blow up with a bare ValueError: not enough values to unpack , and I patched it to raise a clean, actionable error instead. I wrote that fix up, verified it with a stubbed repro, added a --selftest case for it, called it done. Then I went back to write today's articles and actually looked at the line I "fixed" instead of the error path around it. def parse ( text ): meta = {} body = text if text . lstrip (). startswith ( " --- " ): parts = text . lstrip (). split ( " --- " , 2 ) if len ( parts ) < 3 : raise ValueError ( " frontmatter opened with ' --- ' but never closed with a second ' --- ' delimiter " ) _ , fm , body = parts ... split("---", 2) doesn't split on lines that are --- . It splits on the literal substring "---" , anywhere in the text, and stops after the second one it finds. My fix only handles the case where it finds fewer than two — an unclosed fence. It says nothing about what happens when the second "---" it finds isn't the closing fence at all, because a third one showed up first, buried inside a frontmatter value. That's not a hypothetical. I write these article titles myself, and "before/after" is a phrase I reach for constantly: --- title : My Before---After Refactor tags : ai, python, refactor published : true --- real body starts here split("---", 2) finds the em-dash-style --- inside the title before it finds the real closing fence on its own line. So the split points land in the wrong place entirely: >>> from publish_devto import parse >>> meta , body = parse ( text ) >>> meta { ' title ' : ' My Before ' } >>> body ' After Refactor \n tags: ai, python, refactor \n published: true \n --- \n real body starts here \n ' The title got truncated to "My Before" . tags and published never got parsed as frontmatter fields at all — they're sitting in the body now, as literal text, along with the real closing fence and a stray leftover --- . If I ran this thr

AI is disrupting career progression by eliminating the learning opportunities at each rung while simultaneously enabling people to perform above their experience level, Alasdair Allan explained in his talk Engineering Progression When AI Ate the Middle at QCon London. Fewer junior developers join the industry, and AI slows hiring at the entry level. By Ben Linders

A few months ago we shipped the epilot CLI , and it quietly became one of my favorite tools. One command, npx epilot , gives you every single epilot API operation in your terminal: entities, journeys, workflows, pricing, files, permissions, 50+ APIs. Interactive pickers if you're exploring, --json and --no-interactive if you're scripting. It also turned out to be a perfect match for AI agents like Claude. Agents are great at driving CLIs: they discover operations, read the help, make calls, parse the JSON. No custom integration or MCP server needed, the CLI is the integration. And because handing an agent live CRM access is a scary idea, the CLI ships with two safety nets, both enforced server-side: # A session that physically cannot write. The restriction is baked # into the token, so the bearer can't turn it off. epilot auth login --readonly # A token that additionally gets all PII anonymized in every response epilot access-token createAccessToken -d '{ "name": "AI agent token", "read_only": true, "anonymize": true }' Read-only plus anonymized means an agent can explore, analyze and report on your real org all day, and the worst it can do is read data it can't even de-anonymize. Now we've made the CLI even better. On top of the raw API commands, we added app facades : a set of high-level epilot app commands that take you from an empty folder to a working app installed in your org. And that's what this post is really about, because apps are where the fun is. What are epilot Apps? epilot is very configurable out of the box: journeys, workflows, automations, pricing. But at some point every team hits a wall, something the UI simply doesn't offer. A custom tab on the contact or opportunity page showing data from your own systems A whole custom page in the epilot navigation Your own block in the journey builder A widget in the end-customer portal A flow action that calls your API when a workflow step runs An external product catalog or an API proxy to your backend That

Turn product screenshots and prompts into motion videos Discussion | Link

Product analytics told you what happened. We handle it. Discussion | Link

Turn screen recordings into engaging product videos Discussion | Link

Record doctor visits and get clear summaries Discussion | Link