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

Web Scraping with Python in 2026: Best Libraries and Anti-Bot Strategies Web scraping in 2026 looks very different from 2020. Sites are smarter, anti-bot systems are more aggressive, and the legal landscape has evolved. Here's what actually works now. The 2026 Scraping Landscape Challenge 2020 Solution 2026 Solution Bot detection Rotate User-Agent Fingerprint randomization + residential proxies CAPTCHAs Manual solving Turnstile/hCaptcha solvers JavaScript rendering Selenium Playwright (faster, more reliable) Rate limiting Sleep between requests Adaptive pacing + request signing IP blocking VPN rotation Residential proxy pools Best Libraries in 2026 1. Playwright (Best for JS-heavy sites) from playwright.sync_api import sync_playwright def scrape_with_playwright ( url ): with sync_playwright () as p : browser = p . chromium . launch ( headless = True ) page = browser . new_page () page . goto ( url , wait_until = " networkidle " ) data = page . query_selector_all ( " .job-item " ) results = [] for item in data : title = item . query_selector ( " h2 " ). text_content () results . append ( title ) browser . close () return results 2. httpx + Selectolax (Fast, no JS needed) import httpx from selectolax.parser import HTMLParser def scrape_static ( url ): resp = httpx . get ( url , headers = { " User-Agent " : " Mozilla/5.0 " }) tree = HTMLParser ( resp . text ) for node in tree . css ( " .listing " ): print ( node . text ()) 3. API-First Approach (Always check first!) Many sites have hidden or public APIs that make scraping unnecessary: url = " https://www.freelancer.com/api/projects/0.1/projects/active/?query=python " data = httpx . get ( url ). json () Anti-Bot Strategies That Work 1. Request Fingerprint Randomization import random def get_random_headers (): browsers = [ " Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 " , " Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 " , ] return { " User-Agent " : random . choice ( browsers ), " A

GitHub Trending Digest: Optimasi Agent, Parsing Gambar Skala Besar, dan Evolusi Sistem Operasi Selamat datang di edisi digest GitHub Trending minggu ini (30 Juni 2026). Pasar pengembangan perangkat lunak di tengah tahun 2026 menunjukkan pergeseran menarik dari sekadar "membuat model AI lebih pintar" menjadi "membuat sistem AI lebih efisien dan terintegrasi secara mendalam". Tren utama minggu ini didominasi oleh dua tema besar. Pertama, optimasi biaya dan komputasi untuk AI Agents . Kita melihat alat-alat yang berfokus pada pengurangan latency dan peningkatan efektivitas kode yang dihasilkan, dengan filosofi bahwa kode terbaik adalah kode yang tidak perlu ditulis sama sekali. Kedua, kemampuan pemrosesan visual skala enterprise yang melampaui batas konvensi image-to-text tradisional, memungkinkan parsing dokumen kompleks dalam satu langkah ( one-shot ). Di sisi infrastruktur, ada gerakan balik menuju sistem operasi yang minimalis dan deterministik, yang ditunjukkan oleh meningkatnya minat terhadap dokumentasi dan basis kode dari proyek Astrid OS. Berikut adalah lima repository paling populer minggu ini yang merefleksikan tren tersebut. 1. DietrichGebert/ponytail (JavaScript) Star: 68,413 | Tagline: Makes your AI agent think like the laziest senior dev. Repository ponytail telah mengambil alih posisi puncak dengan jumlah bintang yang sangat signifikan. Seperti namanya, alat ini bekerja untuk membuat agen AI berpikir seperti "senior developer termalas" di ruangan tersebut. Filosofi intinya sederhana namun revolusioner: The best code is the code you never wrote (Kode terbaik adalah kode yang tidak pernah Anda tulis). Kenapa Trending? Di era di mana penggunaan LLM untuk generating code sudah menjadi standar, bottleneck baru muncul: hasil generate yang terlalu verbose, kurang efisien, atau bahkan redundan. Ponytail bertindak sebagai lapisan optimasi yang agresif. Alat ini tidak hanya menghasilkan kode, tetapi juga meragukan kebutuhan akan kode tersebut, mencari celah untuk

Breaking long documents into overlapping chunks, preserving context, and reassembling with FastAPI At LectuLibre, we’ve built an AI‑powered platform that translates entire books—EPUBs and PDFs—using large language models. When we first hooked up Claude’s API, we naively fed it a 300‑page PDF in one request. It failed immediately. Claude 3 Opus has a 200K token window, but a 300‑page book can easily run to 300K tokens or more. Even if we squeezed it in, the output would be truncated and the quality would degrade at the extremes of the context window. So we faced a classic long‑document problem: how do you translate a book that’s larger than the model’s context window? Here’s the real approach we ended up with, the code we wrote, and the lessons we learned. The Problem: Token Limits Are Real Claude 3 Opus and Haiku models (and most LLMs) have a maximum context length—200,000 tokens for Opus. A token is roughly ¾ of a word. A 300‑page novel with ~75,000 words translates to about 100K tokens, so it should fit, right? But translations from English to Spanish can expand by 15–20%, and the prompt instructions, system message, and the user message itself all eat into that budget. Plus, we needed to send the entire source text in every call to give the model full context. That’s not feasible. We could have tried a simple split: cut the book at arbitrary page boundaries and translate piecemeal. That fails spectacularly. Narrative breaks mid‑sentence, and phrases like “the previous chapter” lose their referents. We needed a more intelligent chunking strategy. Our Approach: Sliding Window with Overlapping Paragraphs We settled on a sliding window chunking algorithm based on paragraphs, with a generous overlap. Here’s the idea: Split the source text into paragraphs (using \n\n ). Build chunks of max_chunk_tokens (we used 180,000 to keep a safety margin), adding paragraphs one by one and counting tokens with tiktoken . When the chunk exceeds the limit, we start a new chunk but we

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https://www.faa.gov/newsroom/trumps-transportation-secretary...

Anthropic said it would begin restoring access to the Fable on July 1.

Wayve’s offering is part of a growing trend of AI startups using employee tenders as a strategic tool to attract and retain talent.

Anthropic will start its users' access to Mythos and Fable tomorrow, July 1.