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Three fields on a policy decide which attachments ever reach your email agent: { "name" : "Locked-down agent inbox" , "limits" : { "limit_attachment_size_limit" : 26214400 , "limit_attachment_count_limit" : 20 , "limit_attachment_allowed_types" : [ "application/pdf" , "image/png" ] } } Size (that's 25 MB in bytes), count per message, and an allowlist of MIME types. POST that to /v3/policies , attach the resulting policy_id to a workspace, and every Agent Account in the workspace enforces it on inbound mail from then on. Why an autonomous reader needs this more than you do When a human gets a suspicious attachment, there's a judgment step: weird sender, weird filename, don't open it. An email agent has no such instinct unless you build one — and the agents most worth building are exactly the ones that process attachments: parsing invoices, extracting resumes, reading shipped documents. That processing step is the attack surface. A hostile PDF aimed at your parser, a 10,000-page document aimed at your token budget, a zip bomb aimed at your storage — all of them arrive the same way legitimate input does. You can defend in application code, but then every consumer of the mailbox has to get it right, forever. Policy limits on Nylas Agent Accounts (a beta feature) enforce the constraint at the mailbox itself, before any of your code runs. One clarification that saves a support ticket: inbound rules can't do this job. Rules match on sender fields — from.address , from.domain , from.tld — and know nothing about what a message carries. Attachment control lives on the policy, and only there. From policy to enforced, end to end The policy applies through a workspace, not directly to a grant. The full wiring is three calls. Create the policy at /v3/policies , set it as the workspace's policy_id (a PATCH /v3/workspaces/{workspace_id} if the workspace already exists), then create the account into that workspace: curl --request POST \ --url "https://api.us.nylas.com/v3/connect/cus

You have 50 documents and you're running them through an LLM in a loop. The first one finishes at the 2-second mark. The fiftieth finishes at the 100-second mark — not because it's harder, but because it waited in line behind the other 49. Pool runs all 50 at the same time. The Problem With Loops Every developer who works with LLMs writes this code eventually: import os from yait_aichain.models import Model from yait_aichain.skills import Skill skill = Skill ( model = Model ( " claude-sonnet-4-6 " , api_key = os . getenv ( " ANTHROPIC_API_KEY " )), input = { " messages " : [{ " role " : " user " , " parts " : [ " Summarise in two sentences: \n\n {text} " ]}]}, ) documents = [{ " text " : f " Document { i } content... " } for i in range ( 50 )] results = [] for doc in documents : result = skill . run ( doc ) results . append ( result ) It works. It's readable. And it's painfully slow. Each LLM call takes roughly 2 seconds. Multiply that by 50 documents and you're staring at your terminal for almost two minutes. The calls are completely independent — document 37 doesn't need the result of document 12. Yet document 37 sits idle, waiting its turn. That's a scheduling problem, not a computation problem. I ran into this directly while building a task that pulled N files or links and produced a consolidated report. The sequential version was logically fine but just hemorrhaged time. I needed to fire everything at once without rewriting the Skill logic — no new prompt templates, no restructured code, just a different execution model. That's what Pool is. Pool: Parallel Map for LLM Calls Pool takes one Skill (or Chain) and a list of inputs , then launches all of them concurrently. Think of it as Array.map() where every element runs in parallel against an LLM. import os from yait_aichain.models import Model from yait_aichain.skills import Skill from yait_aichain.pool import Pool , DONE , FAILED skill = Skill ( model = Model ( " claude-sonnet-4-6 " , api_key = os . getenv ( "

Our self-proclaimed Scroogian (cheapskate) book-loving writer has put together a guide on finding free books for your Kindle.

TLDR: I had 2,207 GoPro videos, and I need to rewatch them to find interesting moments from my cycling journey. I built a project to index them locally on my M1 Max using open-source ML models, search for those moments, and send the best clips straight to my DaVinci Resolve timeline. I indexed 628 videos (668.68 GB, 15h 13m 18s of footage duration), more details in the metrics table in the last section of this article. Full article: https://iliashaddad.com/blog/i-indexed-669-gb-of-my-gopro-video

Your AI co-founder that remembers decisions + plans for you Discussion | Link

You don't really ever have to explain why a universal remote is a good idea. You have a bunch of stuff that needs controlling; this thing controls them all. Many companies have set out to build a product worthy of this idea, and one product came much closer than most. It was called the Harmony, […]

Our expert sports fan made a detailed guide on what sports you can watch on Apple TV in multiview and how to do so. Plus, some tips for non-Apple TV users too.

Here is how interview goes every single time. Interviewer asks a question: what are the basic principles of OOP? Interviewer always assumes that the answer is "inheritance, encapsulation, polymorphism". These concepts were popularized and have direct connection to C++. C++ was designed by Bjarne Stroustrup. If I try to say that Stroustrup himself said that he didn't invent OOP [1] and try to talk about Simula, Smalltalk and Alan Kay's definition of OOP I always have a concerned look from the interviewer. I'm tired. I don't even have deep understanding of history of programming languages. It's just a one google search that leads to Wikipedia article[2]. It's common knowledge. Why it's a problem every time a have an interview? --- [1] Wired speaks to Bjarne Stroustup : Please note that my claim to fame is not to have invented OOP. I did not - that honour belongs to the designers of Simula: Ole-Johan Dahl and Kristen Nygaard - but I did have a major hand in making it mainstream. [2] https://en.wikipedia.org/wiki/Object-oriented_programming#History submitted by /u/FG3149 [link] [留言]

You see words. A language model sees tokens — chunks of text, usually a few characters each. Everything starts here. Day 2 of my AIFromZero series. Text gets shattered into tokens "unbelievable" → ["un", "bel", "iev", "able"] (4 tokens, not 1 word, not 12 letters) Before any "thinking", your text is chopped into tokens and each becomes a number the model processes. Why not words or letters? Letters : too fine — the model would relearn spelling everywhere. Whole words : too many — millions, plus every typo and name. Subword tokens : the sweet spot. Common words = 1 token; rare words split into reusable pieces. A fixed ~100k-token vocabulary covers any text. The ~4-chars rule (and why it costs you) In English, ~4 characters ≈ 1 token , or ~0.75 tokens per word. This is how everything is priced and limited: API bills are per token (prompt + reply). A "context window" (how much it can read at once) is measured in tokens — 1,000 tokens ≈ 750 words. Verbose prompts and long chat history burn tokens. Concise prompting is a real cost lever. The strawberry problem The model never sees s-t-r-a-w-b-e-r-r-y. It sees a token like straw + berry . The individual letters are buried inside tokens, so counting characters is genuinely hard for it. It's not dumb — it just doesn't read letters. Tokens are step 1 of everything Tokenize → turn each token into a vector (embeddings, next) → run through the transformer → predict the next token. Every LLM starts exactly here. 🔤 Type anything and watch it tokenize live: https://dev48v.infy.uk/ai/days/day2-tokens.html Day 2 of AIFromZero.

The Synthetica Shift: Mastering Prompt Engineering for the AI-Driven Dev Era Introduction The digital landscape has been irrevocably altered. QuantumMind's "Synthetica" isn't just an evolutionary step in AI-assisted development; it's a revolutionary leap, autonomously architecting, deploying, and monitoring full-stack applications from a single natural language prompt. This seismic shift heralds a new era where the traditional lines of software engineering blur. We are no longer solely code creators but becoming high-level system designers and AI orchestrators. The game has fundamentally changed, demanding that we adapt and master the art of communicating with these powerful new systems. This tutorial will explore how to navigate this paradigm by focusing on prompt engineering and high-level system design. Navigating the New Frontier: Prompt Engineering & System Architecture In a world where AI can spin up a complete application stack, our role as developers evolves from writing boilerplate to articulating precise, comprehensive requirements. The "code" we now write is in the form of intelligent prompts, guiding the AI to materialize our vision. This section will walk you through the mindset and practical application of prompt engineering for autonomous development. 1. The High-Level Prompt: Your New Blueprint Gone are the days of starting with npm create-react-app . Your primary interaction begins with a detailed, structured prompt that serves as the architectural blueprint. Think of it as writing a mini-spec document for your AI colleague. Example "Mega-Prompt" for Synthetica: "Develop a secure, full-stack e-commerce application named 'QuantumMarket'. **Frontend (React):** * **User Interface:** Modern, responsive design suitable for desktop and mobile. Implement a clean header (logo, search bar, cart icon, user profile/login button), a product listing page (grid view, pagination, filtering by category/price), product detail pages, a shopping cart view, and a check

The Only Productivity Hack That Actually Worked for Me I've tried every website blocker in existence. They all have the same flaw: one click and they're off. That settings toggle might as well not exist when the urge to procrastinate hits. I needed something I couldn't override. So I built the opposite. The Problem Every blocker follows the same pattern: it blocks, you unblock, you procrastinate. The issue isn't discipline — it's that your future self and your present self want different things. And that future self will happily undo anything you set up. What I needed was a system where both selves agreed on the rules upfront and then neither could break them. The Solution kblocker is a Linux kernel module that hooks into netfilter and drops TCP connections to whatever sites I configure. The key feature isn't the blocking — it's how you turn it off. When blocking is enabled, kblocker generates a 128-bit key. With PGP mode, it automatically encrypts that key to people I trust and then erases it from kernel memory. The raw key no longer exists anywhere on my system. To disable the blocker, one of those people has to decrypt it and send it back to me. I outsourced my willpower. When I want to focus, I run: sudo kblockerctl enable 120 Two hours of blocked distractions. If I feel the urge to procrastinate, I can't — I'd have to text a friend, explain why, wait for them to decrypt it, and paste the result. By then the urge is gone. The Result I've gone from losing entire afternoons to YouTube to actually finishing things. The blocker has caught me mid-reflex to type youtube.com more times than I can count. It's free if anyone wants it: github.com/Dan-J-D/kblocker If you keep breaking your own focus tools, this is for you.

Minesweeper feels intricate — numbers, cascading reveals, flags. Build it and you find it's a grid, a neighbour count, and one recursive function . This is Day 6 of my GameFromZero series. Each cell holds four facts const cell = { mine : false , open : false , flag : false , n : 0 }; n = how many of the 8 neighbours are mines. That number is all the player gets to reason about. Count neighbours once After scattering mines randomly, precompute every non-mine cell's n : let n = 0 ; neighbours ( r , c , ( rr , cc ) => { if ( cells [ rr ][ cc ]. mine ) n ++ ; }); cell . n = n ; Flood-fill is the whole trick When you open a cell with zero neighbouring mines, there's nothing dangerous nearby — so auto-open all 8 neighbours, and if any of those are also zero, they cascade. That's why one click can clear half the board. It's recursion: function open ( r , c ) { const cell = cells [ r ][ c ]; if ( cell . open || cell . flag ) return ; // base case cell . open = true ; if ( cell . n === 0 ) neighbours ( r , c , ( rr , cc ) => open ( rr , cc )); // recurse } This is the same algorithm behind the paint-bucket tool and maze region-filling. Flags + win/lose Right-click toggles a flag (and blocks accidental opens). Click a mine → lose. Win when opened cells = total − mines: if ( cell . mine ) gameOver (); if ( opened === R * C - M ) win (); That's the entire game. Master the state-step-draw loop once and every classic — Snake, Pong, Tetris, 2048, Minesweeper — is an evening each. ▶️ Play it + read the step-by-step breakdown: https://dev48v.infy.uk/game/day6-minesweeper.html Day 6 of GameFromZero.

"Chat with your PDF / your notes / your docs" is everywhere. Today we build it from scratch and you'll see it's just three moves : retrieve, then generate — with one prompt trick that stops the hallucinations. This is Day 46 of TechFromZero. Yesterday (Day 45) we built the retrieval half with pgvector. Today we add the answer half and host it on Supabase. RAG in one line Find the relevant chunks of your documents, paste them into the prompt, and tell the model to answer using only those. That's Retrieval-Augmented Generation. The "augmented" part is just stuffing real context into the prompt so the model isn't guessing from memory. 1. Storage: Supabase is Postgres, so pgvector is one click Supabase is hosted Postgres with an auto-generated API. Because it's just Postgres , vector search needs no separate database: create extension if not exists vector ; create table documents ( id bigserial primary key , content text , embedding vector ( 384 ) ); -- one RPC the app calls to get the closest chunks create function match_documents ( query_embedding vector ( 384 ), match_count int ) returns table ( id bigint , content text , similarity float ) language sql stable as $$ select id , content , 1 - ( embedding <=> query_embedding ) as similarity from documents order by embedding <=> query_embedding limit match_count ; $$ ; 2. Ingest: chunk → embed → store Split your docs into paragraph-sized chunks, embed each with a free local model (all-MiniLM-L6-v2 via Transformers.js — no key, nothing leaves your machine), and insert the row + vector: const embedding = await embed ( chunk ); // 384 numbers await supabase . from ( " documents " ). insert ({ content : chunk , embedding }); Chunk size matters: too big buries the answer in noise, too small loses meaning. A few hundred characters is a good start. 3. Retrieve + Generate (the payoff) Embed the question with the same model, ask Supabase for the closest chunks, then hand them to the LLM: const query_embedding = await embed ( que

AI workspace that’s already connected to everything Discussion | Link

The test passed. The restore completed inside the window. The workload came online. The team signed off, closed the ticket, and filed the results. DR test: successful. And then, somewhere between the test environment and the next real incident, the recovery plan drifted out of alignment with the infrastructure it was written to protect. Not dramatically. Not all at once. Gradually — through a cloud migration, an IdP consolidation, a new SaaS dependency, a network redesign that didn't make it into the runbook. DR plan failure rarely happens where you tested. It happens at the assumptions the exercise never reached. The Test Has a Boundary. The Incident Doesn't. A DR exercise begins with a defined scope. A specific workload. A known starting state. A target environment that has been prepared in advance. The team is available, credentialed, and not managing anything else. The blast radius is controlled before the test starts. A real incident does none of that. Scope expands from the first alert. Authentication problems surface because the IdP that wasn't in exercise scope is now unreachable. Networking issues appear because the failover path assumes a routing table that was updated three months ago. A vendor the plan never named is unavailable, and the recovery sequence stalls waiting for a dependency that was never documented as a dependency. The plan was written for the conditions of the test. The incident arrives in conditions the plan never anticipated. That gap is where DR plan failure actually lives — not in the restore mechanism, but in everything the restore mechanism was assumed to be able to reach. Most DR Plans Depend on Things They Never Recover The recovery exercise validates a workload. What it rarely validates is the recovery infrastructure itself. Consider what a typical enterprise DR plan silently depends on: Assumed — Not Tested: Identity provider, backup management console, cloud account access, ticketing and incident management systems, third-party