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Memory Chips Supply chain strategy from electronics production engineering, 500–50k units/year Introduction "Order from Digi-Key" is a prototyping strategy, not a production strategy. The 2020–2023 IC shortage demonstrated that supply chain resilience must be designed in — not improvised when lead times hit 52 weeks. The Sourcing Tier Structure Tier Examples MOQ Price Premium Lead Time Risk Authorized dist. Digi-Key, Mouser, Newark 1 pc +25–40% 1–3 days (stock) Lowest Franchise dist. Arrow, Avnet, TTI 100–1k Baseline 2–8 weeks Low Manufacturer direct TI, Infineon, ST portals 1k–10k+ −10 to −30% 8–20 weeks Low Regional aggregators IC-Online, local dist. Mixed Variable Variable Medium Spot market Brokers, eBay 1 pc +50 to +500% Days High Never use spot market for ICs without incoming inspection. Counterfeit STM32, ESP32, and common analog ICs are well-documented. Volume Pricing Reality Illustrative for a $2.50 MCU: Volume Digi-Key Arrow/Avnet Manufacturer Direct 100 $3.10 $2.65 N/A 1,000 $2.75 $2.15 $1.85 10,000 $2.40 $1.70 $1.25 50,000 $2.10 $1.40 $0.90 The franchise/direct savings are material at 1k+ units. Establishing Arrow or Avnet relationships pays for the admin overhead within 2 production cycles. BOM Resilience Framework For each critical component, document: Primary source : authorized distribution or direct Secondary distributor : alternative channel for same part Alternate part : functionally equivalent, different manufacturer, validated Buffer stock : target weeks at production rate Lead time worst-case : historical peak, not current During normal periods: 4-week buffer, one secondary source, one qualified alternate. For 5+ year product lifecycles: qualify the alternate before you need it. Practical Sourcing Mix: 500–5k Units/Year Component Type Primary Secondary Notes Commodity passives Digi-Key/Mouser + Yageo/Walsin Arrow Annual pricing agreements MCUs < $3 Arrow direct IC-Online for gap fills 90-day POs, buffer stock MCUs $3–$10 Manufacturer direct + A

Written from months of grinding on shielded liquidity DeFi protocols on Midnight. If you've been trying to build anything serious with shielded fungible tokens on Midnight lending protocols, liquidity pools, DEXes you've probably hit some walls that the documentation doesn't fully prepare you for. The Midnight programming model around shielded tokens is genuinely different from anything in the EVM world, and a lot of the intuitions you carry from Solidity or even other ZK environments will get you into trouble fast. This post is a breakdown of the most impactful errors and misconceptions I ran into while building shielded liquidity DeFi contracts using Midnight's Compact language. These are not theoretical every single one of these either broke a circuit or caused a proof server failure at some point. I'll walk through what the issue is, why it happens, and what the correct pattern looks like. Background: How Shielded Tokens Actually Work Under the Hood Before we get into the errors, let's get clear on the underlying mechanics because this context is what makes the errors make sense. Midnight uses a protocol called Zswap for shielded token operations. When a user sends tokens to your contract by calling receiveShielded , what actually happens is more involved than it looks on the surface. When your circuit calls receiveShielded(coin) , the Compact runtime records a shielded receive obligation in the transaction being constructed. At this point, the proof server kicks in to generate the ZK proof for your circuit. But here's the thing your circuit only describes what the contract side is doing. The transaction still needs to be balanced : the tokens being received by the contract have to come from somewhere. This is where the wallet gets involved through an internal mechanism that runs beneath your circuit. The wallet looks at the ShieldedCoinInfo you're receiving the coin's color (token type) and value and finds a matching UTXO in the user's private coin set. It then

For Job Postings please use this template Hiring: [Location], Salary:[], [Remote | Relocation], [Full Time | Contract | Part Time] and [Brief overview, what you're looking for] For Those looking for jobs please use this template Want to be Hired: [Location], Salary Expectation:[], [Remote | Relocation], [Full Time | Contract | Part Time] Resume: [Link to resume] and [Brief overview, what you're looking for] ​ Please remember that this community is geared towards those with experience. submitted by /u/AutoModerator [link] [留言]

What happens when network calls stop behaving like clean request/response interactions. Timeouts, retries, duplicate side effects, idempotency, backoff, circuit breakers, load shedding, degraded states, observability, etc. submitted by /u/OtherwisePush6424 [link] [留言]

Would you pay $20 a month for access to AI hardware you already own? That appears to be one of Meta's next bets. This week, it quietly announced that your glasses' Conversation Focus feature will soon be limited to three hours of use per month, unless you pay for a $19.99 Meta One Premium subscription. […]

My hackathon journey didn't start with winning. It started with losing. My first hackathon was BlueHacks 2025 . We spent almost all of our time building and very little time understanding the business side of our project. When it came time to pitch, we struggled to explain why our solution mattered. That experience taught me an important lesson: A great product means nothing if people don't understand its value. Next came GCash's invite-only hackathon . We didn't win, but I walked away with something more valuable than a trophy. I learned more about product thinking, working with data, and met someone named Neo, who would later become a key part of my hackathon journey. Then came the YSES Hackathon . Once again, we fell short. We believed we had built a strong solution, but we made the same mistake. We focused too much on the technology and too little on market validation, business models, and the value our product created. Everything changed during Based Space Batch 002 . It was my first international blockchain hackathon, and it completely changed how I approached building products. During the program, Sir Eli Becislao, then Country Lead of Base Philippines, emphasized the importance of storytelling, pitching, and business strategy. That was when I realized hackathons aren't just coding competitions. They're startup simulations. Our team eventually pivoted our idea and built NameThat , a Web3 platform on Base where users could earn rewards for creative names and ideas. Although we didn't win, we received the Most Pivoting Project Award , recognizing how much we improved our solution throughout the competition. That experience became a turning point. Next was the Philippine Blockchain Week ICP Hackathon . Simply being selected as one of the Top 50 teams in the Philippines already felt like an achievement. Then we were invited to present FarmChain on the Philippine Blockchain Week stage. When the results came out, we finished Top 6 out of 50 teams . To some, sixth p

This continues from Part ① . In Part ②, we'll configure the common settings and the internal-processing Power Automate flows. Common Settings Setting Up Connections Open Power Automate ( https://make.powerautomate.com ) Go to "Data" → "Connections" → "New connection" and create a Microsoft Dataverse connection Do the same to create an Office 365 Outlook connection Basic Flow Creation Steps Click "Create" → select "Automated cloud flow" (event-triggered) or "Scheduled cloud flow" (recurring) Name flows in the format [Zone]-[Number] [Description] (e.g., "A-1 Opportunity Stage Stall Alert") Always run a test after creating a flow to verify it works 2. Internal-Processing PA Flows — 4 Flows (Write-back portions of A-4, C-5, C-6, D-3) Once the common settings are done, it's time to build. A-4: Write Back Stage Changed Date Without this flow, the stall-day calculations in A-1 and B-1 will not work. Implement this first. In Microsoft Dynamics 365 (D365), a "stage" refers to a major milestone in a process — such as a sales deal or customer engagement — that guides the responsible person through what needs to happen next. It's how a series of activities is visualized and managed. From here, all work is done in Power Automate. Step Task Details 1 Create the flow "Automated cloud flow" → select trigger "When a row is added, modified or deleted (Dataverse)" 2 Configure trigger Table: Opportunities / Change type: Modified 3 Add condition Add a "Condition" action: "When Status Reason (statuscode) has changed" 4 Write-back action "Update a row (Dataverse)" → set cr917_stage_changed_date to utcNow() C-5: Auto-Set Renewal Date + Auto-Create Renewal Opportunity (on Won) On Won close, two things happen: ① auto-set the renewal date to close date + 365 days, and ② auto-create a new Opportunity for the renewal cycle and add it to the pipeline. Step Task Details 1 Create the flow "Automated cloud flow" → trigger "When a row is added, modified or deleted (Dataverse)" 2 Configure trigger Ta

We're excited to launch PaperQuire — a desktop app that turns plain Markdown into professional, print-ready PDFs. No cloud uploads, no accounts, no subscriptions required for personal use. Why We Built PaperQuire If you write in Markdown, you've probably hit this wall: your content looks great in your editor, but the moment you need to share it as a polished document — a proposal, a report, a spec — you're stuck copy-pasting into Word or fighting with LaTeX. We wanted something simpler. Write in Markdown, click Export, and get a document that looks like a designer made it. No extra steps, no cloud dependency, no learning curve. What PaperQuire Does Live preview — See your formatted document side-by-side as you type. What you see is what you'll get in the PDF. Professional templates — Choose from templates designed for technical docs, proposals, reports, and more. Every template supports custom branding: your logo, your colors, your fonts. Offline-first — Your documents never leave your machine. PaperQuire runs entirely on your desktop — macOS, Windows, and Linux. Plugin system — Extend PaperQuire with plugins for diagrams (Mermaid), math (KaTeX), syntax highlighting, and more. AI Assist — Bring your own API key and get writing suggestions, grammar fixes, and content generation right inside the editor. How It Works Open PaperQuire and start writing Markdown — or open an existing .md file Choose a template and customize your branding (logo, colors, fonts) Click Export to generate a polished PDF instantly Share your document with confidence The entire process takes seconds, not minutes. Free for Personal Use PaperQuire is free for personal use with no restrictions on the core features. The Pro plan adds advanced exports (DOCX, HTML), batch processing, and priority support for teams that need more. Get Started Download PaperQuire for your platform: macOS (Apple Silicon) Windows (x64) Linux (x86_64) Check out the documentation for a quick walkthrough, or just start writi

Existe um desconforto generalizado na área de desenvolvimento. Uma sensação de que o chão mudou, mas ninguém deu o mapa novo. A IA generativa entrou no dia a dia, e de repente aquilo que antes levava horas: escrever funções, montar queries, criar componentes, resolver bugs triviais. Agora passou a levar minutos. Às vezes, segundos. A reação mais comum é: ou "a IA vai substituir todo mundo", ou "não muda nada, é só mais uma ferramenta". As duas posições estão erradas. A primeira é alarmismo. A segunda é negação. O que aconteceu foi uma mudança de papel . O desenvolvedor não deixou de ser necessário. O tipo de contribuição que se espera de um desenvolvedor mudou. E entender essa mudança cedo, especialmente para quem está no início da carreira, é a diferença entre se tornar um profissional de pouco impacto e um profissional indispensável. O modelo que conhecíamos Durante muito tempo, a indústria funcionou com uma divisão razoavelmente clara de responsabilidades: O ciclo tradicional de uma demanda: Alguém identifica um problema → alguém de produto investiga e define o escopo → um arquiteto ou pleno projeta a solução → um desenvolvedor implementa. Cada etapa tinha suas pessoas, suas cerimônias, seus rituais. Refinamento, sprint planning, design review, code review. Não que isso fosse ruim, só era uma estrutura que fazia sentido quando cada etapa era custosa. Dentro desse modelo, a progressão de carreira era mais ou menos assim: Junior recebia tarefas pequenas e bem definidas. Codificava, testava, corrigia. A maior parte do tempo era gasto na execução: a parte braçal . Pleno pegava demandas mais complexas, começava a pensar em como o código se encaixa no sistema. Refatorava, participava de decisões técnicas . Senior definia arquitetura, avaliava trade-offs, mentorava. Codava menos, pensava mais . A IA comprimiu isso. Muito do trabalho braçal que servia como treinamento para o junior agora é automatizado. E isso gerou a pergunta que paira no ar: "Se a IA faz o que eu fazia

Originally published on hexisteme notes . Make every skill, hook, and scheduled job declare four invariants before it ships — Locality (where it can run), Source-of-truth (which facts it owns or borrows), Cross-ref (what depends on it and what it depends on), and Trigger-measurability (whether its trigger is observable at runtime or hidden in external state) — and refuse to hand off any component that leaves one undeclared, because an undeclared assumption is exactly the seam where two components silently disagree. Two separate runtime leaks surfaced in a single audit session, and both traced back to the same root cause: a component that never declared its assumptions. One read configuration from a file that had stopped being the source of truth (so it always returned a stale default); the other was a scheduled job pointed at a remote sandbox while its prompt referenced local-only paths — caught minutes before registration, where any later and it would have billed compute and produced nothing. Neither was a coding bug. Both were missing declarations. The failure mode: components that work alone but leak when combined When you build an AI agent system out of small parts — skills the model loads on demand, hooks that fire on lifecycle events, cron jobs and scheduled routines that run unattended, helper scripts, config profiles — each part usually gets tested in isolation. It works. You move on. The trouble is that "it works" only proves single-shot correctness; it says nothing about whether the part's assumptions agree with the rest of the system. Every component carries hidden assumptions: where it runs (local machine vs. a remote sandbox), which facts it treats as authoritative, what other components it silently depends on, and what its trigger actually measures. When those assumptions go undeclared, conflicts stay invisible until they surface to the user as a flaky, hard-to-trace symptom — the kind that feels like a vicious cycle because every fix in one place re-o

A year ago today, I started at Approov. A hundred days in, I wrote about the transition: leaving management, the refreshing day-to-day feedback loop, the strange experience of relearning a craft I thought I'd lost. I stand by most of it. But a hundred days is enough to notice a change; it takes a year to understand it. So here is what a year taught me that a hundred days couldn't. The rust that mattered At a hundred days I called myself rusty. I was. I reached for patterns that no longer fit and looked up syntax I once knew by heart. I expected that to be the hard part. It wasn't. The rust came off faster than I feared, and somewhere along the way I realised I'd been worried about the wrong thing entirely. The agentic era arrived in earnest this year, and it quietly rewrote the job description. The premium skill is no longer how fast you can produce code from memory. It's whether you can write a precise specification and make a strong architectural decision, then judge honestly whether what comes back is any good. Those are not new skills for me. They are the exact skills that years of reviewing architecture and mentoring engineers had been sharpening the whole time. The craft I sat down to relearn was not the craft that turned out to matter. I spent years assuming management had pulled me away from engineering. It hadn't. It had been quietly preparing me for the version of engineering that was coming. Charity Majors has a name for the shape of this: the engineer/manager pendulum. The idea that a healthy career swings between the two, rather than treating management as a one-way door you walk through once and never come back. I didn't choose when mine swung back. But it swung the right way, and the years spent on the other side weren't lost. They were compounding. A secure transaction is a secure transaction The work itself has been a homecoming of a different kind. I spent years in payments. Now I work in mobile and API security. On paper those are different worlds

Samsung teases wide-as-hell new foldable.

The app's arrival follows an announcement at last year's Meta Connect conference.

Dish, the company that operates Dish TV and Sling TV, has filed for Chapter 11 bankruptcy," as reported earlier by Reuters. The plan will allow the EchoStar-owned company to continue to wind down its wireless operations after "unforeseen delays" held back its sale of $23 billion worth of 5G spectrum to AT&T. Dish TV, Sling […]

The retailer allegedly failed to meet requirements under the Fair Credit Reporting Act.

Most "agent memory" and "agent context" tools today require sending your data to someone else's cloud. If you operate in a regulated, air-gapped, or simply privacy-conscious environment, that rules them out before you've even tried them. I build the opposite: two MIT-licensed, local-first MCP servers that do this work entirely on your own hardware. The problem Agent memory and context assembly are converging on a cloud-only default. That's a non-starter for defense, healthcare, finance, legal, and any team that can't or won't let agent context leave their VPC. It's also just slower and less deterministic than it needs to be: agents re-discover the same facts about your repo and services every session, burning tokens and turns before doing any real work. Mimir: persistent memory, fully offline Mimir is a single ~8MB Rust binary. It encrypts everything at rest with AES-256-GCM, and it works with no API key, no model download, and no network access at all, because the embeddings used for dense search are bundled directly into the binary. It's bi-temporal: every fact carries a validity window, so you can query memory "as of" any past point and supersede facts without deleting history. 43 MCP tools, SQLite + FTS5 hybrid search under the hood. One honest tradeoff worth naming: the FTS5 index needed for fast keyword search currently sits over plaintext, even though the underlying record is encrypted at rest. We're upfront about this in the docs rather than overstating the encryption story. Perseus: compile-before-context Perseus takes a different approach to context than runtime tool-call discovery. Instead of letting an agent rediscover your git state, running services, and test status through a chain of tool calls every session, it compiles all of that into a ready briefing the moment a session starts. The result is deterministic and byte-stable: the same repo state always produces the same compiled context. Honest, reproducible benchmarks On paraphrased queries, Mimir's

The free open source agentic program is finally invading your phone.

3 platforms, one queue, zero editorial control. That was the state of my content automation before I sat down to spec the dashboard. LinkedIn, X, and Threads each had their own generator, their own state files, their own publishing loop. Drafts got generated, passed a quality gate, and fired into the void. If the draft was mediocre or the timing was wrong, I found out after the fact. The problem is not the automation. Automation is why I can run three platform engines without spending two hours a day managing content. The problem is that zero editorial visibility means you cannot catch the bad ones before they post. What I wanted: see every draft before it goes out. Edit inline if needed. Post immediately or schedule for the next slot. Compose something manually when I have a specific take to push. Keep the comment automation untouched because that runs high frequency, low stakes, and babysitting individual replies defeats the point. The spec came out to three core flows. Review queue. Every pregenerated draft surfaces here with full context: platform, topic, generation timestamp, quality score. One click to edit inline, one to approve for the next slot, one to post immediately. The goal is a 30 second review per draft, not a full editing session. Manual compose. Sometimes I know exactly what I want to say. A text area, platform selector, and post button. No generation, no queue, just publish. This is the escape hatch for when something is happening in real time and the pregenerated queue is irrelevant. Schedule view. A simple calendar showing what is queued for which slot across all three platforms. The generator already handles slot logic and quiet hours. The dashboard just needs to surface the state so I can see gaps and move things around without touching JSON files directly. What I deliberately left out: comment automation. That pipeline runs separately, fires frequently, and does not benefit from human review on every reply. Adding it to the dashboard would cr

If you've ever done a timed C++ coding assessment on a platform like HackerRank or DevSkiller, you know the friction isn't really the algorithm — it's the loop . Download a zip with a weird filename, unzip it, hunt for the project root, configure CMake, build, run GTest, fix one failing test, repeat... and somewhere in there you've burned ten minutes of your one-hour window just fighting the harness instead of writing code. These platforms' in-browser editors are fine for quick problems, but for anything involving multiple files (headers, sources, a real test suite), I'd rather work in my own terminal and editor. The catch is that you still have to get the project out of the browser sandbox, build it locally with the exact same toolchain (CMake + GTest), and then package it back up in a way the grader will accept. So I wrote three small bash scripts to remove that friction entirely. Sharing them here in case they save someone else the same ten minutes. The workflow Download the project archive from the platform (zip or tar.gz, filename is whatever the platform gives you — often randomized) Extract it — script 1 handles this regardless of filename or archive type Iterate — script 2 configures CMake once, then repeatedly builds and runs GTest, optionally watching for file changes Package — script 3 strips build artifacts and any local helper scripts, then zips it back up under a name that won't collide with the original download, ready to re-upload Script 1: extract_and_setup.sh Most of these platforms hand you an archive with an unpredictable filename. This script extracts whatever you point it at ( .tar , .tgz , .tar.gz , or .zip ), figures out which directory it unpacked to by diffing the folder listing before and after, and drops the build script into it automatically. #!/usr/bin/env bash # extract_and_setup.sh # Extracts $fname (tar, tgz, tar.gz, or zip) into the CURRENT folder, # then copies run_build.sh into the directory that was created. # # Usage: # ./extrac

If you have ever opened a system design article, seen a diagram with twelve boxes, three databases, a message queue, and the words "eventually consistent," and quietly closed the tab, this post is for you. There is a myth that you need years of experience running large systems before you can learn system design. You don't. Plenty of engineers learn it before they have ever deployed anything bigger than a side project. What you actually need is the right starting point and a way to build intuition without access to production-scale traffic. That is exactly what this guide gives you. "But I've never built anything at scale" Good news: neither had most people the first time they learned this. System design is not a memory test about how Uber works. It is a thinking skill: given a vague problem and some constraints, make a sequence of reasonable trade-offs and explain them clearly. That skill does not require having operated a system serving millions of users. It requires understanding what the moving parts do and practicing the reasoning. The experience helps later, but it is not the price of entry. So drop the idea that you are "not ready." You are ready to start today. The honest minimum prerequisites You do not need much, but you do need these four things. If any feels shaky, spend a few days here first; it will save you weeks of confusion later. What happens when you load a web page. Client sends a request, DNS resolves a name to an address, a server responds. If you can sketch that, you're fine. The two kinds of databases. Relational (tables, rows, SQL) versus non-relational (documents, key-value). You don't need to be an expert, just know they exist and roughly when each fits. What an index is. A way to find data fast without scanning everything. That one sentence is enough to begin. Basic estimation. If something gets a million requests a day, roughly how many is that per second? (About 12, for the record.) The ability to do rough math out loud matters more than