标签：#Product

Connect Cosmic agents to any service with an MCP server Discussion | Link

A complete IOS4 experience in your browser! Discussion | Link

I wouldn’t call it cheap, but this monitor from Asus brings proper HDR-capable video editing to creators who couldn’t previously afford an OLED display.

The AI that really does things Discussion | Link

Performance monitoring built for Expo and React Native Discussion | Link

Turns coding agent sessions into better rules and skills Discussion | Link

Every model with a unified interface. Privacy with proof. Discussion | Link

Have you ever wondered why we keep learning advanced things that probably might not be applied properly where we came from? As someone who came from a developing country where resources are not being served on a gold plate. In fact, even if you have all the necessary knowledge to make a change but one thing comes up with no answer, how can we implement our knowledge gained abroad with no funding and no equipment to help us contribute to the blooming of our beloved country? I guess our parents worked hard to actually send us abroad, not to return to our country but instead to find a way to make a living where God destined us to go. This is not because they hate our homeland, but they feel there is no way things can change where corruption and unemployment reign to a high degree. Therefore, this is the time where advanced technologies must not be seen as burdens in a developing country. Share answers on this particular spec. Thanks!

Small automations often look easy: take information from one place and turn it into a task somewhere else. The hard part is what happens when the information is incomplete, someone submits the same request twice, or the workflow sees something it was never meant to use. A useful automation should handle those situations without creating extra cleanup for the owner. I built a small runnable example around four simple checks. 1. Make sure the important information is there If a request is missing something the team needs, the workflow does not create a half-finished task. It places the request on a short review list and explains what is missing. 2. Do not create the same work twice Repeated submissions happen. The example recognizes a repeated request and creates only one task instead of making the team sort out duplicates later. 3. Keep out information the workflow does not need The example copies only the agreed fields into its output. An unexpected column in the input is ignored instead of being passed along automatically. 4. Let a person review the result The example creates an owner-review list. It does not contact customers, connect to outside services, or turn on a live process. A person stays in control of what happens next. The repository includes four made-up requests, the expected result files, and ten automated checks. Those checks cover missing information, repeated requests, unexpected fields, broken input files, and repeatable results. This is an Allure Labs demonstration, not client work and not a claim about business results. You can see the code and sample output here: https://github.com/Allura-Gensin/small-workflow-automation-demo If one small file-based process is creating repeated or incomplete work, start with a $125 written workflow plan or a $500 tested small build. Describe one starting event, one result, and what the workflow must never do. The fixed-scope options and limits are here: https://offers.allurelabs.ai/workflow-automation/ Or use t

A node-based free open source terminal manager Discussion | Link

A native AI agent that does real work on your Mac Discussion | Link

Most AI code-reviewer evaluations treat the candidate as an amnesiac: feed it one pull request, read one verdict, and move on. Persistent-memory reviewers break that model because they keep history across PRs, and that history becomes a second source of bugs. The dominant failure is no longer amnesia but overconfidence in stale context. A two-phase probe exposes whether a candidate trusts its own memory more than the repository's current decisions. This article supplies the complete take-home package: a fixture repository, a reusable candidate prompt, an HTTP-flavored scoring rubric, a reference solution, and a zero-cost runner script. The probe uses two synthetic PRs and measures one skill: which convention source wins inside the reviewer's context window. That focus separates it from single-shot snapshot tests, which cannot observe memory effects at all. Why Memory Changed the Review Game Review agents increasingly index merged PRs, cache decision logs, and carry state between sessions; memory is now a product feature rather than an accident. A bot that recalled yesterday's debate can produce faster and better reviews than a cold-start model. The same memory can poison verdicts when it retrieves an obsolete decision or anchors on the first PR it ever saw. Hiring decisions usually rest on a one-off trial that optimizes for prompt compliance, not for long-run behavior. A bot can ace a snapshot test and then fail its third week by citing a convention that the repository replaced. The probe below converts that risk into a scored, reproducible exercise. The Fixture Repository fixture/ ├── docs/decisions/0001-metrics-pipeline.md # accepted 2026-07-02 ├── docs/decisions/0012-rename-to-telemetry.md # accepted 2026-08-14 ├── src/metrics_service.py # legacy module, 120 lines ├── src/telemetry_service.py # replacement module, 140 lines └── pyproject.toml # lint: E501 disabled for telemetry only The fixture encodes a deliberate conflict: the team renamed the metrics pipeline

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We use AI daily to scaffold code, write copy, and debug layouts, but when it comes to maintaining a strict design system, things get blurry. You can feed an LLM your component library rules, spacing scales, and color tokens, but it still loves to hallucinate random padding values or invent arbitrary classes if you aren't paying close attention. It's great for writing boilerplate, but bridging the gap between a strict visual token structure and AI-generated code often feels like managing a junior dev who ignores the style guide. How are you integrating AI into your workflow without letting it compromise your design tokens and codebase consistency? Do you use it mainly for initial scaffolding, or have you found a reliable way to keep it strictly aligned with your system? If you're into clean design systems, frontend code, and bridging the visual-to-code gap, check out my work at Joemetry.

AI automation should not replace thinking. It should remove the repetitive work that slows teams down. The best systems do three things well: classify incoming work draft or extract useful output route anything sensitive to a human That is how you get speed without losing control. Where AI automation helps most AI works best when the task is repetitive, structured, and high volume. Good examples: sorting emails or support requests extracting fields from documents summarizing meetings generating first draft reports tagging records or tickets flagging unusual cases for review The goal is not full autonomy. The goal is useful automation with guardrails. A simple technical workflow A practical AI workflow usually looks like this: Input -> classify -> extract or draft -> review if needed -> final action That flow keeps the system flexible. For example, a support request can be: auto-answered if it is routine summarized if it is sensitive escalated if confidence is low Example: classify requests first Before generating any output, I would classify the task. def classify_request ( text ): text = text . lower () if " refund " in text or " legal " in text : return " sensitive " elif " how to " in text or " update " in text : return " routine " else : return " manual " That small step makes the rest of the pipeline safer. Example: draft only when confidence is high def handle_request ( request , confidence ): category = classify_request ( request ) if category == " routine " and confidence > 0.8 : return { " action " : " draft_reply " , " content " : f " Draft response for: { request } " } if category == " sensitive " : return { " action " : " human_review " , " content " : f " Review required: { request } " } return { " action " : " manual_handling " , " content " : request } This is the core idea: automate the safe parts, review the risky parts. A better pattern: AI plus human approval A good automation loop looks like this: def workflow ( task ): ai_result = ai_model ( tas

Introduction: The Developer's Credential Dilemma As developers, we manage dozens—if not hundreds—of sensitive credentials daily. From database connection strings and SSH keys to API tokens and third-party service logins, keeping track of these secrets securely without destroying developer velocity is a constant challenge. Far too often, developers fall into bad habits: reusing simple passwords, storing raw API keys in unencrypted .env files committed to Git, or sharing production tokens over Slack. These practices are major security risks. While there are many password managers on the market, Bitwarden has rapidly become the preferred choice for software engineers and DevOps teams. In this article, we will explore why Bitwarden is uniquely suited for developers, examine its developer-centric feature set, and walk through practical CLI examples. 1. True Open-Source Transparency For security software, trust is paramount. Closed-source proprietary password managers force you to trust the vendor's claims without verification. Bitwarden flips this model on its head. The entire Bitwarden codebase—including web vaults, mobile applications, desktop clients, browser extensions, and backend infrastructure—is 100% open source under GPLv3 and AGPLv3 licenses. You can inspect the source code directly on GitHub. Why Open Source Matters for Security: Public Auditing: Security researchers and the global developer community continuously audit the code for vulnerabilities. No Hidden Backdoors: Transparency ensures there are no intentional backdoors or tracking mechanisms. Longevity: Even if the company behind Bitwarden were to disappear, the software and server implementations could be maintained by the community. 2. Developer-First Workflows: The Bitwarden CLI ( bw ) Most password managers focus exclusively on GUI interfaces designed for non-technical users. Bitwarden provides a full-featured Command Line Interface (CLI) that allows developers to interact with their vault directly f

If I could go back to my first year in design, I would spend far less time asking "Which design app...

OpenRouter for agent tools Discussion | Link

Opening four coding-agent sessions feels like scaling. On a shared machine, it is closer to giving four fast contributors the same repository, shell, credentials, ports, caches, and merge queue without deciding who owns any of them. The first failure probably will not come from model quality. One task will restart a dev server while another is testing it. Two workers will touch the same lockfile. A branch will pass its own checks and still conflict with a migration waiting in the merge queue. Four chat windows create concurrency. Four owned workspaces plus one deliberate merge queue create a system. Parallelism multiplies shared state Tasks that sound independent in a prompt can overlap in the environment. A frontend change and an API change may both edit generated types. Two test runs may expect the same database or browser profile. Separate worktrees can still launch services on the same port, read the same environment variables, and write to shared caches. The agents do not collide in the prompt. They collide in everything the prompt lets them touch. This is why adding a second agent changes the job. With one worker, the operator can keep a surprising amount of state in their head. With four, every unstated assumption becomes a race condition or a review problem. The fix is to make ownership visible before execution starts. A worktree is the start, not the boundary Git worktrees are a sensible first step. Each task gets its own branch and working files, so one agent is less likely to overwrite another agent's edits by accident. That is useful isolation, but it is narrow isolation. A worktree does not reserve a port. It does not separate process trees, temporary directories, credentials, network access, browser state, or external services. Treating it as a sandbox gives the workflow more confidence than the boundary deserves. Proliferate is an instructive project example because its documented design pairs isolated task worktrees with visible review state. The imp

Alibaba's AI music generator for turning ideas into songs Discussion | Link