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I'll preface to say I'm not at all a programmer, so pardon me for saying anything stupid The increasing amount of slop is no secret, and but software as a whole, be it plugins or apps or even whole operating systems, has become increasing plagued by a set of kinda-unrelated problems that feel like they fall under the same umbrella. I'll keep it short, so practical issues of efficiency, privacy, security, and long-term support, and then ethical issues of being human-made. I personally believe that we're in need of novel, widespread terms to be able to describe the kind of software that in principle does not have certain traits. I attached the link because I believe the term "open source" is analagous to what I'm talking about here. Everybody has an idea of what it means and entails, but it also doesn't mean that everything has to strictly follow it to a T, because it's just a word at the end of the day. It's a pledge to the user to abide by principles of being "Open Source," and I'm saying that we need new words that similarly serve as principles such as that of, optimization, long-term support, being human-made, etc. (It's kind of like how we have terms like organic, free-run eggs, free range eggs, and of course you would end up with having a bunch of terms that dont mean anything at all, but i'm confident that a few will emerge as "standards" in the same way the term open source is.) This could go hand-in-hand with maybe making it common practice to at least disclose the development process when publishing software. In (science) research, we're beginning to see the implementation of "AI Disclosures" to detail all such usage, to be published with the paper. surely practices like these should be encouraged in software development as well. I'm sure there are (smaller) discussions on what exactly should constitute as "best practices" to the people that care about such things, but I've yet to see mainstream terms that go beyond only vaguely describing what we dont like

I spend a lot of time preaching architecture and constraints, so it is always nice when a side project gives me receipts. Adding this new feature to DumbQuestion.ai was a good reminder that a well-structured first version lets you spend your next iteration on value, not repair. Below, you will find a few relatively simple challenges and how thoughtful, upfront design made the changes effortless. To vibe or not to vibe ... Many developers jump right in and just rip out an app, ship fast, let the coding agent sort it out, come back and deal with it later. To be fair, that absolutely can get you to first release faster. But even on a solo project, a little proper SDLC discipline pays back later when you want to extend the product without turning every feature into a rescue mission, which is a theme that already runs through how I have been building DumbQuestion.ai. Extend this to the enterprise and you turn a little upfront effort into potential huge savings on token spend Roasting starup pitches (for sport) ... The core idea for Startup Roast was simple enough: take a startup pitch, roast it, and add a reality-check section so the output is not just mockery for mockery’s sake. To illustrate (and avoid just vaguely describing the feature) I picked a random but highly upvoted pitch from Product Hunt: Vida . Vida, which pitches itself as an “AI clone” that learns how you work, remembers what matters, and becomes a “second you,” with early use cases like Reply Rescue, Prompt Rescue, Resume Rescue, Workspace Cleanup, and Daily Wrap. This is a pretty common target use case of agentic AI making it a solid candidate. If you want to skip ahead, here's an example roast for Vida. Combining a preliminary web "market search" into the content yielded a result that was not just sarcastic, but informed. The roast hit the obvious AI-clone positioning, questioned whether the product was really a clone versus a macro suite, and then turned the market context into a sharper Reality Check

I've been slowly adding some new free tools to Moxie Docs (partly for SEO, partly to illustrate some of our feature sets before any commitment) for some reason this mermaid editor one blew up on Google rankings so I figured I'd share in case people find it useful! We also have ADR, AGENTS.md, LLMs, and a few other free tools.

Meta's new AI detector has rate limits for some reason.

How is it going for you all working on projects for this platform? I’ve read that sometimes they ban you without even giving you a heads up, is that true? Also, I’d like to ask about the tests they give you for software projects before accepting you; are they actually very difficult? Do I need to have AI level problem solving skills or something similar to get accepted? How is the hourly pay working out? Is it truly hourly, and are deposits made weekly? Thanks for reading, everyone; I’m curious about remote work and want to get started. submitted by /u/ClarkKennedy929 [link] [留言]

submitted by /u/lood9phee2Ri [link] [留言]

The new image-generating model has numerous use cases, including advertising, decorating and creator-based opportunities.

As part of Meta’s Muse Image model rollout, Instagram users with public accounts need to opt out to block AI generations of their content.

Even though Netflix is the world's most popular paid streaming service, the company has been struggling to keep viewers watching its series after their first seasons. Beef - the streamer's anthology about people locked in feuds - lost 70 percent of its viewership when it returned earlier this year. There seems to be some confusion […]

TL;DR Collapsed a billing model from à-la-carte features to plans-only, in four safe phases. Unified authorization across web, API, and MCP so all three obey one permission layer. Fixed a legacy Oracle password-sync writing to the wrong column. Four repos moved today. Here's the thread that ties most of them together: one source of truth beats two. Billing: plans-only Spent most of the day migrating a SaaS off per-feature à-la-carte subscriptions and onto plans-only entitlements. The interesting part isn't the model — it's doing it without a billing outage: seed plans, switch reads to plans, backfill every org, then delete the old machinery. Expand/contract, four deployable phases. Full write-up in the focused post. One permission layer for three surfaces An ops tool exposed the same actions three ways — web UI, API, and an MCP server for agent access. The bug: each surface checked authorization slightly differently, so an MCP tool could allow something the web UI blocked. The fix was to make the MCP tools gate on the same permission layer as everything else, so: web ─┐ API ─┼─► one permission check ─► allow / deny MCP ─┘ TL;DR: web ≡ API ≡ MCP — three doors, one lock. Also added a dedicated support-engineer role scoped for debugging without handing over the keys to everything, plus identity/diagnostics/SLA read tools so an agent can answer "why didn't this notification send?" without shell access. Before After Each surface authorizes its own way Single permission check, shared MCP tool could out-permission the UI MCP bound to the same guard No debug-scoped role support_engineer role, read-only diagnostics Legacy Oracle password sync Smaller but sharp: a password reset was writing to the wrong Oracle column and also touching a date_modified field it had no business updating. Routed the student reset to the correct password column and dropped the stray write. Lesson with legacy schemas — the column that looks right and the column the app actually reads from are not a

TL;DR Moved a SaaS from à-la-carte feature subscriptions (pay per feature) to plans-only (pay for a tier, get its features). Did it in four phases so nothing broke mid-flight: seed plans → gate on plans → migrate orgs → delete the old machinery. Lesson: model migrations are safest as expand → migrate → contract , not a big-bang swap. The problem The old billing let an org subscribe to individual features à la carte. Flexible on paper, painful in practice: entitlement logic had two sources of truth (per-feature subscriptions and an implicit plan), and every gate had to check both. Time to collapse it into one model — you buy a plan , the plan carries the features. The trap with this kind of change is the temptation to rip out the old columns and ship. Do that and every in-flight subscription, every gate check, and every webhook that still speaks the old language breaks at once. The phased plan I ran it as four ordered migrations. Each phase is deployable on its own and leaves the app working. Phase What it does Why this order F1 Seed Plans into the prerequisite chain New model must exist before anything reads it F2 Gate features on the plan, not the feature-sub Reads switch over while writes still dual-run F3 Deploy op migrates existing orgs onto a plan Backfill — nobody left on the old model F4 Remove the à-la-carte machinery Contract — safe only after F3 This is the expand/contract pattern applied to a domain model, not just a schema. Expand (F1) adds the new thing alongside the old. Migrate (F2–F3) moves reads then data. Contract (F4) deletes the old thing once nothing points at it. F1 seed F2 gate on plan F3 backfill orgs F4 drop features ┌────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ plans │ --> │ reads: plan │ -> │ every org on │ -> │ delete a-la- │ │ exist │ │ writes: both │ │ a real plan │ │ carte code │ └────────┘ └──────────────┘ └──────────────┘ └──────────────┘ safe safe safe safe Gating on the plan The gate collapses to a single question

Redpanda is a Kafka-API-compatible streaming platform written in C++ with no JVM and no ZooKeeper. This guide installs Redpanda on Ubuntu 24.04, secures it with a Let's Encrypt certificate and SASL/SCRAM authentication, tunes the kernel for production, verifies with a producer/consumer test, and exposes Redpanda Console behind Nginx basic auth. By the end, you'll have a secured, production-tuned single-node Redpanda cluster with a web console. Prerequisite: Ubuntu 24.04 server sized per Redpanda's CPU/memory requirements , non-root sudo user, and a domain A record (e.g. redpanda.example.com ). Install Redpanda $ sudo apt update $ curl -1sLf 'https://dl.redpanda.com/nzc4ZYQK3WRGd9sy/redpanda/cfg/setup/bash.deb.sh' | sudo -E bash Warning: Only run vendor setup scripts you trust — piped curl | sudo bash runs with root privileges. $ sudo apt install redpanda -y $ rpk --version Open the Firewall Port Service Purpose 9092 Kafka API Producer/consumer traffic 8082 Pandaproxy (HTTP) REST access for non-Kafka clients 8081 Schema Registry Avro/Protobuf schema versioning 9644 Admin API Monitoring, config, health checks 33145 Internal RPC Inter-node communication $ sudo ufw allow 9092,8082,8081,9644,33145/tcp $ sudo ufw allow 80/tcp $ sudo ufw allow 443/tcp $ sudo ufw reload Issue a Let's Encrypt Certificate Redpanda ships with plaintext networking by default, fine for a lab, not for anything else. $ sudo apt install certbot -y $ DOMAIN = redpanda.example.com $ EMAIL = admin@example.com $ sudo certbot certonly --standalone -d $DOMAIN --non-interactive --email $EMAIL Certbot stores certs under /etc/letsencrypt/live , readable only by root. Redpanda runs as its own redpanda user, so copy the certs into a dedicated directory: $ sudo mkdir /etc/redpanda/certs $ sudo cp /etc/letsencrypt/live/ $DOMAIN /fullchain.pem /etc/redpanda/certs/node.crt $ sudo cp /etc/letsencrypt/live/ $DOMAIN /privkey.pem /etc/redpanda/certs/node.key $ sudo cp /etc/letsencrypt/live/ $DOMAIN /chain.pem /etc/re

Azure Storage SFTP is Microsoft's managed file transfer service on top of Azure Blob Storage, convenient, but billed continuously per endpoint (roughly $0.30/hour, ~$220/month) and tied to Azure AD. SFTPGo is an open-source file transfer server offering SFTP, FTP/S, and WebDAV with pluggable storage backends (local disk, Azure Blob, S3-compatible, GCS) and no per-endpoint charge. This guide deploys SFTPGo with Docker Compose and Traefik, sets up user auth (password + SSH key + 2FA), connects S3-compatible object storage, and covers the migration path from Azure Storage SFTP. By the end, you'll have a self-hosted file transfer server with the same capabilities at zero endpoint cost. Azure Storage SFTP → SFTPGo Mapping Azure Storage SFTP SFTPGo Equivalent Notes SFTP Endpoint SFTPGo SFTP Server Configurable port, default 2022 Azure Blob Storage Azure Blob backend Native support; point at the same container, no migration needed Azure AD Authentication LDAP/OIDC plugin External identity provider via plugin Local Users Web UI / REST API user management Hierarchical Namespace Virtual directories No HNS requirement Azure Monitor Built-in logging + webhooks/syslog Prerequisite: Linux server with Docker + Compose, a DNS A record for your domain, and (if migrating) an existing Azure Storage account with SFTP enabled plus the Azure CLI installed locally. Deploy with Docker Compose 1. Create the project directories: $ mkdir -p ~/sftpgo/ { data,config } $ cd ~/sftpgo 2. Create the environment file: $ nano .env DOMAIN = sftp.example.com LETSENCRYPT_EMAIL = admin@example.com 3. Create the Compose manifest: $ nano docker-compose.yml services : traefik : image : traefik:v3.6 container_name : traefik command : - " --providers.docker=true" - " --providers.docker.exposedbydefault=false" - " --entrypoints.web.address=:80" - " --entrypoints.websecure.address=:443" - " --entrypoints.web.http.redirections.entrypoint.to=websecure" - " --certificatesresolvers.letsencrypt.acme.httpchallenge=tr