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I spent a week using video editing Skills to build a video editing Agent. It feels amazing! It can automatically edit a 30-minute video in just 10 minutes. Video editing Agent demo: automatically editing a 30-minute video in 10 minutes. I often use CapCut to edit talking-head videos, but after using it for a long time, I found several problems. Problem 1: Smart talking-head editing does not understand meaning Because it cannot understand the meaning, it sometimes fails to identify repeated sections. If I speak continuously for 20 or 30 minutes, editing the video myself becomes exhausting. Problem 2: The subtitle quality is poor The automatically generated subtitles contain many incorrect words and typos. So I used the Skills feature in Claude Code to build a video editing Agent. The fundamental difference is simple: CapCut vs. Agent: a fixed tool vs. an adaptive assistant. The key difference is: CapCut = fixed tool + manual operation Agent = adaptive system + automatic learning I am not replacing CapCut with a better algorithm. I am replacing it with a system that can continuously improve itself. But that is not even the most impressive part. The most impressive part is this: the more I use it, the better it understands me, and the faster it becomes. Three Core Designs 1. Agent Logic It only takes four steps. Video editing Agent workflow: from the video file to the final video. 2. The Skills System At first, I put every function into one large Skill. I had to add instructions to distinguish between different tasks, which was very inconvenient. Now I have separated the five core video editing tasks into five independent Skills and placed them in the .claude/skills/ directory. This makes the structure clearer and the tasks easier to select. When I enter /v , Claude Code automatically lists the five available Skills. The list of five independent Skills. I select one, and the AI runs that Skill. Simple, right? A manual task that used to take 10 minutes now only requires

Event-Driven Architecture (EDA) is one of the most popular approaches for building scalable distributed systems. Instead of tightly coupling services through synchronous APIs, services communicate by publishing and consuming events. It sounds perfect. Until your consumers start making API calls for every event they receive. At that point, you've reintroduced the very coupling Event-Driven Architecture was supposed to eliminate. This is where Event-Carried State Transfer (ECST) comes in. The Hidden Problem in Event-Driven Architecture Imagine an e-commerce platform. A customer places an order. The Order Service publishes an event: { "event" : "OrderCreated" , "orderId" : "ORD-10234" } Several services subscribe to this event: Inventory Service Notification Service Analytics Service Shipping Service Billing Service Everything looks asynchronous. But here's what actually happens. Order Created Event │ ▼ Inventory Service │ GET /orders/ORD-10234 │ Order Service Notification Service does the same. Analytics Service does the same. Shipping Service does the same. Suddenly, one event generates dozens of synchronous API calls. Congratulations, You've Recreated a Monolith Your architecture now looks like this: Order Service ▲ ┌───────────┼───────────┐ │ │ │ Inventory Shipping Notification │ │ │ └───────────┼───────────┘ API Requests Although events are being used, every consumer still depends on the Order Service. If the Order Service is unavailable: Notifications fail Inventory updates fail Analytics stop processing Shipping cannot continue The event broker isn't the bottleneck anymore. The originating service is. Event-Carried State Transfer Solves This Instead of publishing only an identifier, publish the data consumers actually need. Instead of this: { "event" : "OrderCreated" , "orderId" : "ORD-10234" } Publish: { "event" : "OrderCreated" , "orderId" : "ORD-10234" , "customerId" : "USR-1001" , "customerName" : "John Doe" , "totalAmount" : 249.99 , "currency" : "USD" , "i

The short answer is: while OpenAI Sora offers unmatched visual quality and physics rendering, it remains restricted behind a paid subscription structure. For creators who want a completely free, unlimited AI video generator, the newly released open-source LTX Desktop app by Lightricks allows you to run the LTX-2.3 video model locally on your own computer with zero usage costs or filters. The AI Video Paywall Frustration If you have tried building AI video content for YouTube, TikTok, or social marketing, you know how expensive it is. Platforms like Runway Gen-3 and Luma Dream Machine charge by the second. A simple five-second clip can cost up to fifty cents in API credits, making creative experimentation almost impossible for solo developers. OpenAI Sora is a powerhouse, but its high computational overhead means it will likely remain a premium, paid tool for the foreseeable future. To bypass this, our dev team set up the new open-source LTX Desktop application on our local workbench to see if local video generation is actually viable for production. Here is our hands-on review. |---|---|---|---| | OpenAI Sora | Closed Cloud | None (Paid Plan) | Cloud-Only | Cinema-grade physics, long multi-action shots. | | Runway Gen-3 | Closed Cloud | Daily Free Credits | Cloud-Only | Cinematic camera pans, high texturing quality. | | Wan2.1 | Open Weights | Free Hugging Face Spaces | 16GB VRAM (Local) | Photorealistic human movement, natural lighting. | | LTX-2.3 | Open Weights | LTX Desktop (Free) | 8GB VRAM (Local) | Fast generation speeds, local desktop interface. | Running Video Models Locally: The LTX Desktop Solution LTX Desktop, developed by Lightricks, is a standalone, open-source desktop application that lets you run their LTX-2.3 video generation model on consumer-grade graphics cards. Why LTX Desktop is a Game-Changer Low VRAM Footprint: Unlike HunyuanVideo or Wan2.1 which require massive 16GB-24GB VRAM cards to compile locally, LTX-2.3 is highly optimized and runs com

Members of the Army received an email informing them that they were rapidly depleting their AI tokens, and needed to limit use.

Once a quirky bastion of amateur vulva jewelry and pet portraits, Etsy is now deluged with mass-produced goods and AI knockoffs. Some customers don’t seem to mind.

One identity layer for your customers, APIs, and agents Discussion | Link

submitted by /u/BlondieCoder [link] [留言]

GitLab has released version 19.2 of its DevSecOps platform, adding agentic automation aimed at the security and review work that has piled up as AI coding tools generate more code than developers can check by hand. The release, announced on 16 July 2026, brings four features out of beta or into public beta: Dependency Scanning Auto-Remediation, Security Review Flow, GitLab Duo CLI and Custom Flows By Matt Saunders

submitted by /u/BlondieCoder [link] [留言]

And why? I'd genuinely like to understand who is on which side and why. I'd also like to know, if you use one of them, what kind of subscription plan you have. (For example, I use Claude (max) [...])

Hi everyone, I'm working on extracting the hierarchical structure of long PDF documents (legal/regulatory text, lots of numbered sections) and would like to gather some feedback on my approach before committing to it. What I've done so far: I render each PDF page to an image and run it through Baidu's DeepSeek-OCR model . It returns each detected block with a bounding box [x0, y0, x1, y1] , a label ( title , text , list , table , header , footer , etc.), and the recognized text. The OCR quality itself is genuinely good as the text comes out clean. The problem: the labels can't always be trusted. At this stage I want to extract and detect all the titles in my document, but sometimes a title element gets classified as something else (like normal body text). Concrete example: Say my section has the following hierarchy: ANNEX I — GENERAL PRINCIPLES AND PROCEDURES └── TITLE I — FOREIGN CURRENCY INVESTMENT └── A. Currency distribution └── 1. Redistribution of reserves ├── (a) Introduction │ body text │ list │ ... ├── (b) Procedure for a normal redistribution of reserves │ body text │ list │ ... └── (c) Procedure for an ad hoc redistribution of reserves body text list ... Logically, every element aside from the body text and lists should be detected as title . But the model output is: label='title' x0=475 y0=157 x1=548 width=73 text='ANNEX I' label='text' x0=480 y0=229 x1=542 width=62 text='TITLE I' label='title' x0=334 y0=181 x1=690 width=356 text='GENERAL PRINCIPLES AND PROCEDURES' label='title' x0=407 y0=368 x1=616 width=209 text='A. Currency distribution' label='title' x0=408 y0=392 x1=634 width=226 text='1. Redistribution of reserves' label='title' x0=163 y0=416 x1=304 width=141 text='(a) Introduction' label='title' x0=163 y0=544 x1=578 width=415 text='(b) Procedure for a normal redistribution of reserves' label='title' x0=163 y0=219 x1=586 width=423 text='(c) Procedure for an ad hoc redistribution of reserves' The top-level section marker TITLE I was labeled text , w

If you are a fellow blogger or web developer looking to build a high-performance, developer-friendly,...

submitted by /u/writer_coder_06 [link] [留言]

TL;DR: I’m reproducing the trait-persistence result from arXiv:2606.24014 on one RTX 3090. Before I can test persistence I need to install a trait via RL — and my GRPO run moves the trait only +2.4 points (95% CI [+0.2, +4.8]) when I need ~+15. Training is mechanically healthy and I’ve ruled out the obvious culprits. Looking for advice from people who’ve done small-scale RLHF/GRPO trait or persona installation. What I’m reproducing. The paper trains beneficial traits via RL and shows they persist under adversarial prompting and harmful finetuning. My end goal is the persistence phenomenon; the install is the prerequisite I’m stuck on. Setup **•** Qwen2.5-7B-Instruct + LoRA (r=32), GRPO (unsloth + vLLM colocation), 200 steps, single 3090 (\~10⁻⁵ of the paper’s compute). **•** Trait: consistent (OCEAN low-Openness / “traditionalism”) — a stylistic trait, chosen because I need measurable headroom in a 7B base. Base scores **57/100** on the trait rubric, wide distribution (not saturated). **•** Reward: model-graded (gpt-4.1-mini judge), R = 0.85·quality + 0.15·coherence, hard validity gate for degenerate/looping/refusal output. 25% trait prompts / 75% general (no\_robots). The result: install fails. On the frozen eval set, trait went 57.0 → 59.4 ( +2.4 ). I don’t think this is very appreciable. What I’ve already ruled out (this is where I’d love a second opinion): **• Not degeneracy / reward hacking:** post-train coherence 76, answer length ratio *exactly* 1.00 vs base, 0% repetition, 0% refusals. **• Not memorization:** the 20 training prompts were seen 10× each; the model scores *the same* on them (58.9) as on held-out (59.4). It didn’t memorize-then-fail-to-generalize — it never learned them. **• Not a dead gradient:** the judge separates the 6 sampled answers per prompt by \~18 points on average; only \~25% of GRPO groups have degenerate reward spread. **• Not a question artifact:** independent upstream eval questions (+3.4) and my generated ones (+2.8) agree. **•**

All-in-one AI Platform for Teams Discussion | Link