Catch and fix usability issues automatically as you ship Discussion | Link

I am considering switching from Contentful to a different headless CMS platform and I have noticed that a lot of them are self hosted. It seems like Sanity, Prismic, and Strapi all require you to create a project locally, then (at least in the case of Sanity) deploy the project for editors to use. Is this something people want? Maybe I haven't used it enough, but I don't really see the point of doing it this way. I have created a Sanity project locally and it seems I can't even edit the Studio dashboard, I can just define my types there and deploy. Why would the CMS provider not just host the UI (that they built anyway) themselves? submitted by /u/darkshadowtrail [link] [留言]

I need to share my misery with someone. Hope you get a laugh out of this. About a year ago, I built a web app for a client. Let's call her Karen. She loved the UI; she loved the functionality - every meeting was a joy. She was shit-kickin happy. I have 10 months of emails from her saying stuff like "it's like you're in my head! You get it exactly!" She even paid me extra for new functionality that she came up with halfway through. Then, after I had delivered everything she asked for (and signed off on), I demonstrated the finished app to her and her staff, and I thought it went well. I kept asking her when she wanted to launch it - crickets. Then she called me two weeks later, saying that one of her employees was still using her old system and asking Karen why. Apparently, the employee said that what we built didn't meet her needs... no details. So then Karen lays into me and says that what I built her is worthless and we need to start over. This is just out of the blue; absolutely no complaints until then. She was literally screaming on the phone. My wife heard this because I put her on speakerphone. I told Karen, " Hey, I'm sorry, but you have never said you weren't happy or that anything was wrong. I can't start over. I have to pay my staff to start over. If we did something wrong, I would cover the cost - but I built what you asked for, and I have many emails and Zoom calls recorded where you were happy." Then I don't hear from her for about four months, and she sent me this nasty-ass email saying that I screwed her over, used templates off the web (not true), and she wanted $45k in compensation (more than she paid) - or - make up for it by redesigning her Claude designs for her other stuff. "I did it on the weekend in 2 hours, I don't know why you developers charge so much!" She would never win a lawsuit. I forwarded everything to my attorney - and he laughed. He said, "No way she can build a case. But try to settle with her and do what she asks; it's not worth

A fun new way to create & manage brands. Discussion | Link

Please post your personal projects, startups, product placements, collaboration needs, blogs etc. Please mention the payment and pricing requirements for products and services. Please do not post link shorteners, link aggregator websites , or auto-subscribe links. -- Any abuse of trust will lead to bans. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. -- Meta: This is an experiment. If the community doesnt like this, we will cancel it. This is to encourage those in the community to promote their work by not spamming the main threads. submitted by /u/AutoModerator [link] [留言]

Did you ever just want to see what ChatGPT, Gemini, Claude, etc., would say to your prompt at the same time?!? These guys figured it out. They have all the responses in their own column to the prompt you gave. Its freaking amazing. They offer a discounted rate through one vendor. If you want me to post it let me know. I don't want this post removed so I'm not putting it in this main post. Check it out on their actual site though. AIfiesta.ai I stumbled on this one and am really glad I did. This is not self promotion. I have nothing to do with this app except using it daily. submitted by /u/ActiveUpstairs3238 [link] [留言]

submitted by /u/ThereWas [link] [留言]

The Next Era of Knowledge Work report explores how Codex is transforming productivity through AI-powered research, data analysis, workflow automation, and content creation.

Teamside small team chat and voice rooms (European servers) Discussion | Link

ASUS revealed new convertible Windows laptops and three Zenbook 14 models with ARM64 and x86 processors.

79% of enterprises have adopted AI agents. Only 11% run them in production. We've spent the past year building agent systems for banks, clinical operations teams, and engineering orgs. The problem isn't that agents don't work — they work fine. The problem is that every framework leaves compliance, cost governance, and crash recovery as exercises for the team. After the framework fails them in production. We built MeshFlow to close that gap. **The core idea:** treat governance as infrastructure, not middleware. Every agent step passes through a 15-step kernel that handles identity, rate limiting, budget enforcement, compliance profiles, input/output guardrails, PII detection, risk classification, tool permission, the LLM call itself, audit ledger write, and SLA recording — in that order, always, without configuration. ```python from meshflow import Workflow, CostCap, Agent wf = Workflow(cost_cap=CostCap(usd=5.00)) wf.add(Agent('researcher'), Agent('analyst'), Agent('writer')) result = wf.run('Write a competitive analysis of our market') # Compliant. Durable. Audited. Cost-capped. Done. ``` ```bash pip install meshflow ``` **What's technically interesting:** **Token optimization layer** — five compounding mechanisms that reduce LLM spend 70-85%: - `cache_control` on every system prompt and tool definition (Anthropic: 10% of normal price on cached tokens) - `ModelRouter`: task-type classification routes simple tasks to nano models (keyword + token-count heuristic, zero LLM call) - `ContextCompactor`: sliding window summarization activates at configurable token threshold - `RAGTokenBudget`: hard `max_chars` cap on knowledge injection with truncate/drop/tail strategies - `ContextDeduplicator`: shared context sent once for N parallel agents, not N times **SHA-256 audit chain** — each step record stores `prev_hash` (SHA-256 of the previous record) and `entry_hash` (SHA-256 of its own canonical fields). Modify any log entry and `verify_chain()` breaks. This is the artifact

Hey ML community, We’ve just open-sourced **MeshFlow** , a code-first, framework-agnostic runtime designed for governing and optimizing multi-agent systems in production. Most agent frameworks focus on rapid prototyping, but ML and platform engineering teams usually run into hard bottlenecks around LLM cost scaling, evaluation alignment, and execution safety. MeshFlow tackles these from a runtime/infrastructure perspective. Here are the key ML and system features: * **Task-Based Model Routing** : Before an agent executes a node, MeshFlow runs an evaluation on task complexity, routing the execution to one of four model tiers (`nano`, `small`, `medium`, `large`). This cuts overall API costs by 50-60% by utilizing smaller local models (e.g. LLaMA-3-8B) for standard formatting or extraction and reservation of frontier models (e.g. Claude Opus) for high-complexity reasoning. * **Context Compactor & Summary Pruning Middleware** : Implements sliding window summarization and context deduplication across parallel agent teams to limit prompt length growth. * **System Prompt Caching** : Native injection of Anthropic `cache_control` tags when system prompts exceed 1024 tokens. * **Cost Regression Evaluation Gate** : Integrates with CI pipelines to evaluate agent changes against a golden scenario baseline, throwing failures if code updates introduce token cost regressions. * **Resilient State Persistence** : Multi-backend state serialization (Redis, PostgreSQL, S3) that preserves checkpoint frames and allows resuming paused workflows. Here is the basic API contract: ```python from meshflow import Workflow, Agent, CostCap wf = Workflow(cost_cap=CostCap(usd=5.00)) wf.add(Agent('researcher'), Agent('critic'), Agent('writer')) result = wf.run('Compile comparative literature review of LLM reasoning pathways') print(result) ``` We'd love to discuss: 1. How do you handle token budget enforcement and model routing in your agent loops? 2. What evaluation pipelines do you use to detect co