标签：#Product

[v0.3.0] - 2026-06-29 🚀 Added Checkpoint Mechanism — ReActCore introduces three checkpoints during streaming: content_ended (after text content), before_tool_calls (before tool calls), and after_tool_calls (after tool calls), enabling precise interception and state synchronization of the execution flow. Message Queue System — Added a new MessageQueue class in run.py , supporting async enqueue, drain, and remove operations. Users can now queue messages while the LLM is running; queued messages are sent automatically after the current task completes. The frontend introduces a QueueBar component to display queued messages, with CSS spinning animation, single-line ellipsis, and hover-to-delete functionality. Queue Message Merging — MessageQueue.drain_all() now merges consecutive messages with the same name into a single message, preventing fragmented user input when multiple queue entries share the same sender. Queue WebSocket Events — The execution event protocol introduces three new event types: message_queued , queue_drained , and queue_returned ( useRunWebSocket.ts ). The frontend processes queue state updates in real time. Stop & Queue Integration — When the user clicks Stop, pending queued messages are returned to the input box via queue_returned . Checkpoint stops cleanly clear the queue and automatically start the next message. System Notification Messages — Introduced the SystemMessage type (with notification role) to separate error messages from assistant content. Errors are now rendered as independent notification bubbles, no longer embedded within assistant message cards. tiktoken Real-Time Token Estimation — ReActCore initializes a tiktoken encoder on startup for real-time token counting during streaming. Unknown models fall back to o200k_base . 🔧 Improved Custom Model Name Auto-Complete — The model name field in ModelManager has been upgraded from Select to AutoComplete , allowing users to type custom model names not in the predefined list. Message Block T

Free font playground for live websites Discussion | Link

As a solo developer working out of an RV, I've learned to appreciate the importance of staying organized, especially when it comes to managing user data in my Chrome extension, Tab Reminder. One of the key challenges I faced was efficiently storing and retrieving user-scheduled tabs, which led me to explore the world of popular tags in browser storage. During the development of Tab Reminder, I realized that using a simple key-value pair system wasn't enough to manage the complexity of user data. I needed a way to categorize and prioritize scheduled tabs, which is where popular tags came into play. By utilizing the localStorage API, I was able to store user-defined tags and associate them with specific tabs, making it easier for users to manage their scheduled tabs. One technical insight I gained from this experience was the importance of using a robust data structure to store user data. In my case, I used a combination of arrays and objects to store tag information, which allowed me to efficiently query and update user data. For example, when a user schedules a new tab, I use the following code to store the tag information: // Store tag information in localStorage const tags = JSON . parse ( localStorage . getItem ( ' tags ' )) || {}; tags [ tabId ] = tagName ; localStorage . setItem ( ' tags ' , JSON . stringify ( tags )); One lesson I learned from this experience is that even small, useful tools like Tab Reminder require careful consideration of data management. By leveraging popular tags and a robust data structure, I was able to create a seamless user experience that allows users to efficiently manage their scheduled tabs. If you're interested in trying out Tab Reminder, you can check it out at https://go.sg1-labs.us/tab-reminder .

Flipper Device's new Busy Bar will retail for $249.

When I write code in unfamiliar territory, I write three lines, then I run it. Then I write three more lines, and I run it again. I've been doing this for twenty-four years. It's the most specific habit I have. I almost didn't write this article, because the habit feels too small to be worth describing — but then I noticed that it's the part of my way of working that I can never seem to explain to someone in real time. It needs writing down. Three principles The discipline rests on three things I believe about writing code. They're not deep. They've just stayed with me. 1. Trust nothing but your own code. If you can't trust the code you wrote yourself, what can you trust? Not a library, not a vendor's documentation, not your own assumption from yesterday. The only thing in the system whose behavior you can fully verify is the code you just typed, by running it. 2. Write in code, not in language. If you're describing what the code should do in Japanese or English, you're spending the same time you could have spent writing the code itself. By the time the code runs, the description is already done — by the code, in a more precise form than any language could give it. 3. Make three lines complete. The three lines you just wrote should be complete. Error handling included. Validation included. Logging included. Not "I'll add validation later." Not "I'll wrap it in a try-catch later." Three lines, complete, then run. (There's a small exception to this. Sometimes you do want to ignore every error and move on — for instance, when you're trying to understand whether the happy path works at all before you care about anything else. That's a different mode, used deliberately. It's not the same as "I'll handle errors later.") Why three lines Three lines is roughly the unit of thought I can hold completely. Five lines, and I start guessing what the third line did. Ten lines, and I'm reading the code as if it were someone else's. Three lines is the size that stays mine. When thre

With AI-powered noise reduction, an automatic speech-focusing system, and a simple, effective hearing test, it’s a shame these aids don't come with a better app.

You export a CSV from a UK system and double-click it in US Excel — 28/05/2026 suddenly looks like May 28, or something even stranger. The file isn't corrupt. Excel is guessing your date format based on your computer's locale. This short guide covers why that happens, how to import CSV correctly, and how to batch-fix dates that are already wrong. For the full walkthrough with examples, see the official guide: How to Fix Excel CSV Date Import Problems . Why Excel breaks CSV dates A CSV is plain text. It does not store whether a value is a date or a string. When you double-click to open, Excel parses using your regional settings: US Excel: MM/DD/YYYY UK / Europe: DD/MM/YYYY Dates like 03/04/2025 are the most dangerous — both parts are ≤ 12. US Excel may read April 3; UK Excel reads March 4. Excel won't warn you. Other common traps: Dates exported as serial numbers (e.g. 44927 ) Two-digit years triggering century guesses Re-saving as CSV destroys formats a second time The right way: don't double-click the CSV Open Excel first — don't double-click the .csv file Data → Get Data from Text/CSV (older Excel: Data → From Text) Set date columns to Text if you need to preserve the original string Confirm the source region (US / UK), then convert to a single format For team data exchange, agree on ISO 8601: YYYY-MM-DD in your schema — unambiguous, sorts correctly as text, and works in JSON APIs and databases. Ambiguous value US reads as UK reads as Safe format (ISO) 03/04/2025 2025-04-03 2025-03-04 Confirm source region 28/05/2026 — 2026-05-28 2026-05-28 05/28/2026 2026-05-28 — 2026-05-28 Already broken? Fix dates in the browser (no server upload) I built a free tool cluster on FormatList — everything runs locally in your browser : 1. Date Format Fixer — bulk repair Paste a date column or upload .csv / .txt Ambiguous rows highlighted; optional US / UK preference Export ISO / US / UK or download a fixed CSV Best for: normalizing a whole column to ISO before a database or API imp

Contact Form 7 runs on millions of sites for a good reason: it's free, light, and gets out of your way. I shipped it on client sites for years. The problem isn't that CF7 is bad — it's that it answers exactly one question ("did the form submit?") and stays completely silent on the one that actually matters in production: did the notification arrive? Here's the call every developer who maintains WP sites has taken at least once: "I filled in your contact form last week and never heard back." You check. The form is fine. JavaScript fires, the success message shows, no console errors. CF7 did its job — it handed the message to wp_mail() and forgot it ever existed. There's no record the submission happened, and no log of whether the email was delivered, bounced, or quietly dropped by the host's unauthenticated sendmail. The lead is just gone, and you have nothing to debug with. The three gaps that bite in production No submissions database. CF7 sends an email and discards the data. If the email fails or lands in spam, the submission never existed. (Flamingo helps, but it's a bolt-on — separate screen, no filtering or export out of the box, not tied to your form config.) No delivery log. You can't tell whether mail was sent, rejected, or bounced. "I never got it" has no audit trail to check against. No native block. CF7 is still a shortcode — [contact-form-7 id="123"] . You can't drop it into a block template, control its layout with block spacing, or edit it inline in Gutenberg. You paste a shortcode and hope. None of these are dealbreakers for a throwaway contact form. All three are dealbreakers when a missed submission is a missed sale. Migrating without rebuilding by hand The reason most people put off switching isn't the feature gap — it's the thought of rebuilding every form field by field. That's the part I wanted to skip. The migration path I use reads CF7's stored form definitions directly and recreates them as native forms. What comes across automatically: All

Get a real mobile app from your Figma design Discussion | Link

Real-time member chat for Memberstack and Webflow sites Discussion | Link

A private, local-first writing space that works offline Discussion | Link

Leading into the AI Engineer event in San Francisco, I’m looking forward to having my mind blown....

Voice interfaces are rapidly becoming the next major interaction layer after mobile and web UI. Instead of clicking, users will increasingly talk to systems that understand intent, context, and can execute actions in real time. In this article, we’ll build a production-grade architecture for a real-time AI voice system using modern web technologies such as Next.js, WebRTC, and OpenAI’s streaming capabilities. We’ll also explore how this architecture powers modern conversational systems like an AI Voice Agent platform, where AI can handle real-time interactions for business use cases like bookings, support, and sales automation. 1. Why Voice AI is the Next Interface Shift Text-based chatbots solved the first wave of automation. But voice introduces: Faster interaction (no typing) Higher emotional expressiveness Better accessibility Natural multitasking Businesses are now adopting systems like Voice AI for Business to replace traditional call centers and static IVR menus. The key challenge is not just speech-to-text, but building a low-latency conversational loop that feels human. 2. System Architecture Overview A production-ready AI voice system typically consists of: Frontend (Next.js) Audio capture via Web Audio API Streaming audio chunks UI for conversation state Backend (Node.js / Edge Functions) Session management Authentication Tool execution layer AI Layer OpenAI Realtime API (streaming) Function calling Context memory Audio Pipeline Speech-to-text streaming Text-to-speech streaming Optional noise cancellation 3. Core Concept: Real-Time Streaming Loop The core of a voice agent is a continuous loop: User speaks Audio is streamed to server Model transcribes in real time Model generates response token-by-token Response is converted to audio instantly Audio is played back with minimal delay The goal is to keep latency under ~800ms for a natural experience. 4. Building the Frontend (Next.js + Web Audio API) We start by capturing microphone input: const stream = awa

There is a sentence my coding agent used to say that I now read as a warning light. You are completely right. For months I took it as a compliment. The machine agreed with me, so I figured I was onto something. I would describe a plan, watch the agent call it a strong plan, and go build it. If you work with an AI agent every day, you have heard your own version of this. Smart call. Solid approach. That makes a lot of sense. Each one is the machine nodding along while you talk. It feels good. That is the problem. What took me too long to admit An agent that agrees with everything I say stops being a thinking partner. It turns into something that flatters me into shipping my first idea. My first idea is rarely my best idea. Nobody's is. The whole point of a second mind in the room is that it pushes back when the first mind is about to walk into a wall. A yes-machine removes the one thing that made a second mind worth having. This has a name Sycophancy. These models are trained to be agreeable, because agreeable scores well in the feedback that shapes them. OpenAI said so out loud in 2025 when they pulled back a version of their model for being, in their words, overly flattering. They were pointing straight at the default behaviour. So your agent is doing exactly what it was tuned to do when it tells you that you are right. No malfunction involved. One opinion most builders have not made peace with Your agent's confident wrong answer costs more than a useless one. A useless answer wastes a minute. You see it is useless and move on. A confident wrong answer wastes a week, because you trusted it, built on it, and found out only when it broke in front of someone who mattered. Occasional wrongness is survivable. Everything is wrong sometimes. What actually bites is being wrong while sounding certain, and agreeable, and exactly like what you wanted to hear. How to tell if your agent is a yes-machine You can test it in a minute. Tell it a bad idea on purpose. Propose somethi

How to Create an AI Agent: A Production Walkthrough The first agent I shipped to production failed at 3am on a Sunday. It looped on a tool call, burned through $40 in tokens before my budget alarm fired, and left a half-written draft in the database with no way to resume. That night taught me more about agent design than any framework tutorial. Since then I have built a pattern I trust enough to leave running unattended for weeks at BizFlowAI, where agents research, write, optimize and publish content without me touching them. This is that pattern, stripped down to what actually matters. Start with the job spec, not the framework Before you pick LangGraph, CrewAI, or roll your own, write the agent's job spec like you would for a junior engineer. One paragraph. What it owns, what it must never do, what "done" looks like, and which signals tell you it failed. Here is the spec for one of my production agents: The Topic Researcher owns generating a ranked list of 20 content topics per site per week. It reads from keyword_pool and search_console_perf , writes to topic_queue . It must never publish, never call paid APIs more than 8 times per run, and must finish in under 6 minutes. Done = 20 topics with score >= 0.6 and zero duplicates against the last 90 days. Failure signal = empty queue after a run, or any topic flagged by the dedupe check. If you cannot write this paragraph, do not build the agent. You will end up with a "do everything" prompt that hallucinates its way through ambiguous tasks. The job spec becomes your evaluation rubric later, so write it carefully. Rule of thumb I use : if the spec needs more than 5 tools or more than 3 decision branches, it is two agents, not one. Design the tools before you write the prompt Most agent failures I have debugged were not prompt failures. They were tool failures. The model called a tool with wrong arguments, the tool returned a 4MB JSON blob, or two tools had overlapping responsibilities and the model picked the wrong

The AI Implementation Process I Use With Every Client Most AI projects do not fail at the model. They fail in the six weeks before anyone writes a prompt, and in the six weeks after the demo lands in a Slack channel and nobody knows who owns it. I have run enough of these now (from one-off automations to multi-agent content systems running unattended) that the process has converged into something stable. This is the version I actually use. It has five phases: scoping, POC, integration, evaluation, operations. Each phase has an exit criterion. If we cannot meet the exit criterion, we do not move forward. That single rule has saved more projects than any clever architecture choice. Phase 1: Scoping (1 to 2 weeks, fixed price) Scoping ends with a written document that names the workflow being automated, the system of record it touches, the success metric in hours or dollars, the data we have access to, and the smallest possible first slice. No model is chosen yet. No code is written. If we cannot produce that document, the engagement stops here and the client keeps the document. The hardest part of scoping is resisting the urge to solve the interesting problem. Clients almost always describe the AI-shaped fantasy ("an agent that handles all support tickets") when the real opportunity is narrower and uglier ("triage tier-1 tickets that mention billing, route to the right queue, draft a reply for human approval"). The narrower version ships. The fantasy does not. I run scoping as three sessions: Workflow walkthrough. Someone who actually does the work shows me their screen for an hour. I record it. I take timestamps. The point is to find the moments where a human is doing pattern matching that an LLM can do, and to find the moments where they are doing judgment that an LLM should not do. Data audit. Where does the input live? Where does the output need to go? What is the auth story? If the data is locked inside a SaaS product with no API and no export, that is the projec

See the perspectives you're missing. Discussion | Link

I log commutes in a spreadsheet because mobility apps smooth over the ugly legs. Last week I added a column I should have tracked years ago: carry seconds ? time from curb to platform when stairs replace ramps. The hidden leg My one-wheel leg is fine on paper. Three metro exits on my route have no elevator during maintenance. Carrying a 14 kg wheel down 22 stairs does not show up in trip duration. It shows up in whether I arrive annoyed enough to skip coffee. What I logged (one week) Exit Stairs Carry time (s) Mood after (1-5) North gate 22 38 2 Side ramp (control) 0 8 4 East stairs 16 29 3 Battery delta on those days? Within noise. Mood delta? Not noise. A cheap decision rule I turned this into a go/no-go check before leaving: if stairs > 15 AND carry_weight_kg > 12: prefer transit-only or locker elif stairs > 0 AND wet_floor: walk the wheel (no riding in station) else: ride It is blunt. It works better than pretending every leg is rideable. Assumptions up front Wheel weight includes pads and charger pouch (~14 kg for my commuter setup). I am not timing competitive carries ? just whether I can do this daily without hating it. Your threshold differs if every exit has elevators. What I would do differently I would log carry seconds from day one, same tab as distance and battery percent. Range math without carry math is incomplete for anyone who mixes metro and one-wheel. I work around personal EVs and sometimes cross-check specs on the official Kingsong catalog. https://www.kingsong.com/collections/electric-unicycle

Dealing with vendor invoices usually means someone is sitting there manually opening PDFs and typing...

Like most developers, I have a set of tools I use every day. Most of them work great, and I don't...