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Biggest AI firms will likely recoil at Bernie Sanders' AI wealth fund.

OpenAI introduces new spend controls and usage analytics for ChatGPT Enterprise, helping organizations manage costs and scale AI with confidence.

Karamo Brown, famous for his pep talks on Netflix’s “Queer Eye,” has jumped into the wellness and AI space with his new app, Kē. After spending a year and a half focusing on his own journey—from fitness and nutrition to meditation, sobriety, relationships, and personal growth—Brown wants to help others do the same. Kē offers […]

Ship AI agents you can actually see Discussion | Link

My team (around 40 people who write code) is evaluating tools for AI-assisted code review. The market appears to be rife with options, so before starting a series of free trials, I’d like to ask a knowledgeable crowd. What tools or services are you using? Do you use them just for code review, or for other purposes as well, such as incident response or branch management? Why did you choose them, and what do you like or dislike about them?

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

Thinking about indexing, we know a only a big picture that uses B-Tree structures in memory, but in modern DBMs systems there are some enhancements introduced in indexing where systems like PostgreSQL are utilizing some new OS system calls like io_uring to make syncrhonous IO reading, also direct retrieval of record from disk using functions like fseek() and in memory optimization of traversing like applying binary search on leaf page. PostgreSQL always keeps a file for indexing, unlike MySQL where it only keeps an indexing file for non-clustered indexing, and clustered indexing is calculated directly from the table, it is interesting to study the indexing file structure as well Index file structure has line pointers, TID called as tuple ID Tuple is the column value, the same column you registered for indexing, by using which Postgres calculates the actual physical address of the records from the disk to fetch it directly. Postgres has another interesting feature, while creating an index, it always sorts data and maintains ranges of pages in page 0 of the indexing, where Postgres can identify which page to look for and then load that page into memory, and then it becomes a leaf page, then binary search is performed to get the actual TID so that the record can be fetched directly For an in-depth explanation about the index file structure, loading the index file into memory, and then searching for the actual record's address, check out the given link submitted by /u/Ok_Stomach6651 [link] [留言]

The software engineers filed a complaint with Seattle’s civil rights office accusing Amazon of illegally retaliating against them for expressing their personal political beliefs.

When three Amazon software engineers testified earlier this month at Seattle City Council hearings about data centers, they started their testimony by citing a city law barring employment discrimination over political speech. Now, they're accusing their employer of breaking that law by retaliating against them. On June 10th - one week after the hearing, and […]

Most SaaS ideas don’t fail because of bad ideas. They fail because the execution gets stuck in an endless setup loop. You start with energy, then slowly get buried in: auth systems, billing, dashboards, SEO, analytics, and infrastructure decisions. By the time the “real product” should begin, momentum is already gone. Here are 7 practical alternatives to building SaaS in a way that never gets finished. 1. Nexora (start with a working SaaS foundation) Instead of rebuilding everything, Nexora gives you a production-ready base so you can focus on actual features. Includes: Authentication system Stripe billing User dashboards SEO pages Blog + docs structure Clean Next.js architecture 🔗 https://nexora.collabtower.com/ 👉 Best for founders who want to ship instead of setup. 2. Build-from-scratch Next.js projects The most common approach. You get: Full control Flexible architecture But you also get: Weeks of setup Repeated boilerplate work High chance of burnout before launch 3. SaaS boilerplates (minimal versions) Lightweight starter kits with: Auth Basic UI Simple Stripe setup But usually missing: Real dashboards SEO systems Production-level structure 4. Supabase-first builds Backend-focused setups. You get: Database Auth APIs But still need to build: Billing UI system Marketing pages SaaS structure 5. Low-code SaaS tools Fast visual builders. Pros: Quick UI creation No heavy coding Cons: Limited flexibility Hard to scale complex SaaS logic Platform dependency 6. AI-generated starter apps AI tools can scaffold SaaS apps instantly. Pros: Fast starting point Cons: Inconsistent structure Requires cleanup Not production-ready out of the box 7. Tutorial-based SaaS builds Many developers still learn SaaS by following tutorials step-by-step. Pros: Educational Cons: Slow Fragmented Hard to turn into real production apps Final takeaway Most SaaS workflows fail before launch because they repeat the same mistake: They start from zero every single time. That creates unnecessary setup

Bash is the easiest language for AI to write and the easiest language to get devastating output from. A 20-line script that "just cleans up old files" can recursively delete a home directory because the model assumed a variable would always be set. A "simple log shipper" can write your secrets to a remote server because the model used set -x for debugging and forgot to remove it. I have run AI-generated bash that I should not have. Most engineers I know have too. After enough close calls, there's a short checklist that catches the worst of it. This is that checklist. The five things to check before running any AI-generated bash 1. Does it start with a strict pragma? The first lines of any non-trivial bash script should be: #!/usr/bin/env bash set -euo pipefail IFS = $' \n\t ' What each does: set -e — exit on any command failure. Without this, a failure in line 5 doesn't stop the script from happily running lines 6-50. set -u — error on undefined variables. This is the one that saves you from rm -rf $UNDEFINED/ . set -o pipefail — propagate failures through pipes. Without it, failing-command | grep something succeeds because grep succeeds. IFS=$'\n\t' — sane field splitting. Defends against word-splitting bugs in filenames. If the AI-generated script doesn't have these, add them and re-read the script. You'll often discover bugs the pragma now flags. 2. Is every variable expansion quoted? # Wrong rm -rf $TARGET_DIR # Right rm -rf " $TARGET_DIR " The wrong version is what causes the "I deleted the root directory" stories. If $TARGET_DIR is empty or contains a space, the command becomes rm -rf (delete current directory) or rm -rf foo bar (delete two unintended things). Models default to the wrong version about half the time because the right version is harder to write in chat ("escape the quotes!") and the wrong version is what most blogs show. Fix: When reading AI bash, mentally check every $VAR for quotes. Add them if missing. This is the single biggest source of bas

# EnvForge BETA v1.1 ⚡ Structured .env scaffolding for modern applications. Generate, validate, and protect environment variables for 14+ services – without ever opening a docs page. Github repo [ https://github.com/Jos3456/envforge ] NPM version (https://img.shields.io/npm/v/envforge-dev) MIT License (https://img.shields.io/badge/License-MIT-yellow.svg) ## Installation bash npm install envforge-dev **Requirements:** Node.js 18 or later. --- **## Quick Start** bash # 1. Generate an .env file and choose your providers envforge init # 2. Fill in your actual credentials envforge fill # 3. Check everything is set correctly envforge validate ## All Commands ### Scaffolding Command What it does envforge init Create a new .env by selecting providers interactively envforge add <provider> Add variables from a specific provider to your existing .env envforge preset Generate a .env from a popular stack preset envforge example Create a safe‑to‑commit .env.example file envforge fill Interactively enter values (secret keys are masked) envforge list Show all built‑in and custom providers ### Guardrails Command What it does envforge validate Check that all required variables are filled in envforge scan Detect secret keys accidentally exposed in frontend code envforge hook install Install a pre‑commit hook that runs validate + scan ### Customisation Command What it does envforge provider add Create a custom provider template envforge registry update Download the latest providers from the community registry ## Built‑in Providers Category Providers Database Supabase, Neon, MongoDB Atlas Auth Clerk, Auth0, Firebase AI OpenAI, Anthropic (Claude) Payments Stripe Email Resend, SendGrid Storage Cloudinary, AWS S3 / Cloudflare R2 Other Vercel Missing a provider? Add your own with envforge provider add or contribute one to the community. ## Framework‑Aware Scanning Use --framework for smarter detection: # Next.js specific rules (app/ vs pages/, "use client") envforge scan --framework next Th

MapTap is a phone game that will make you feel smarter after you play it.

You walk into the RFP briefing. Your competitor has 200 people, 97% AI coverage, and a 4-day delivery promise. You have 15 people and a proposal you haven't even finished writing. Do you bet on better tech, or on understanding people better — and playing dirtier when you have to? This story is your answer. Act I · The Crack When Finova's RFP landed, everyone in the industry knew how big this was. Cross-border payment system. Multi-currency settlement + compliance + risk. Their last deployment had a P0 incident — an exchange rate module drifted by four decimal places in an edge case, and audit chased it for two months. So Finova's CTO made it clear: a $1.8M contract, and whoever signs off owns the result. $1.8M. Enough to keep a small testing company alive for a whole year. Plenty of firms showed up at the briefing. But only two were real contenders. QualiGuard — mid-sized, just closed their Series A, 200 people, their own AI testing platform called Aegis. A $1.8M contract was barely a rounding error for them — but with Series A money comes the pressure to show revenue growth for the next round, and Finova was a trophy client in the cross-border payments space. The case study was worth more than the project itself. Derek stood at the podium, flipping through slides packed with numbers: Aegis delivers 97.2% test automation coverage. Full Finova platform testing in four business days. No "we'll try." Just "we can do it." VeriTest — small, fifteen people. Marcus spent the whole morning working the room with Finova's people. I sat in the back row with nothing. Marcus slid back over and leaned in: "Their PPT makes yours look like a joke." I didn't answer. I was watching Derek's boss. Sarah — QualiGuard's VP, Derek's direct supervisor. She sat in the front row, off to the side, and never once looked at Derek during his entire presentation. She was on her phone. As one of the few women running a technical department, I watched her longer than I watched Derek. When Derek fla

Quick update to anyone interested in upcoming changes to the PAGI project (spiritual successor to Plack/PSGI). 1) Distribution split up: when we released PAGI, we initially released everything as one distribution. PAGI ( https://metacpan.org/pod/PAGI ) currently has a) the PAGI specification; b) the reference server and c) a bunch of ease of use tools, similar to the role that the Plack distribution played for PSGI. Putting everything into one place was just to make my life easier as in the early bunch of releases there was a lot of fixes and updates, most of which cut across all three parts of PAGI. Also I wanted to make it easy for people getting into PAGI to be able to explore the ecosystem. However now that code seems to be settling down having these in independent repos and releases makes more sense. Going forward the PAGI repo will only update if the spec itself changes; PAGI::Server and PAGI::Tools (where all the utilities and helpers now go) likewise. I think this will start to bring some stability to the ecosystem, especially now that PAGI::Server is functionally complete based on the goal chart I had for it initially. So I will only update it to fix bugs and security issues. PAGI::Tools will probably continue to see evolution over the summer as I start to nail down more common use cases and identify patterns worth encapsulating. 2) Specification clarifications and updates: The PAGI specification itself will move to v0.3 in the next release and it contains mostly clarifications and fixes. Biggest change will be a more detailed mechanism for controlling streaming output, especially around handling back pressure as well as new callbacks to notice when the output buffer is getting full and when it clears. Hopefully these changes will make it easier and more reliable to do streaming in PAGI. PAGI::Server has been updated to match, and the response helper in PAGI::Tools has some updates around that as well. Currently all this sits on Github: https://github.com/j

This is Lowpass by Janko Roettgers, a newsletter on the ever-evolving intersection of tech and entertainment, syndicated just for The Verge subscribers once a week. When Fox announced its acquisition of Roku earlier this week, executives of both companies were quick to promise that not much would change in the near future. Sure, getting its […]

This is Part 13 of my series on the Microsoft Agent Framework. You can read the original post over on lukaswalter.dev . In the previous article , we looked at workflows. Workflows make sense when the process itself needs structure: state, checkpoints, events, human approvals, and resumable execution. This post is the bridge from Agent Framework into RAG. I plan on doing a full RAG deep dive sometime later. The practical question for now is smaller: How do I connect an Agent Framework agent to private application knowledge without stuffing every document into the prompt? For agents, RAG is less about adding more text and more about giving the agent a controlled retrieval path. The agent should fetch the right context at the point where it needs it. Agents do not know your private data Your company documents, product catalog, tickets, rules, policies, runbooks, and internal knowledge base live outside the model. The model has generic knowledge. Your application has private knowledge. Treat those as separate systems. You can paste some private data into the prompt, and for a demo that may be enough. But this falls apart quickly: full documents are expensive to send repeatedly long prompts are fragile stale documents may sit next to current ones users may not be allowed to see every source long context still needs selection The last point is easy to underestimate. A larger context window lets you send more text. It does not decide which text is correct, current, relevant, or permitted. Do not give the agent all knowledge. Give it the right context at the moment it needs it. Retrieval owns that job. The minimal RAG shape The basic RAG loop is small: user question -> retrieve relevant chunks -> pass chunks to the agent -> agent answers using that context For documents, the longer pipeline usually looks like this: documents -> chunks -> embeddings -> vector store -> search -> retrieved context -> agent response Documents are split into smaller chunks. Those chunks are embe