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Our frontend builds crossed 20 minutes. The obvious move was to rip out Webpack for something faster. We didn't. Before touching a single line of config, we profiled the entire pipeline. Webpack's ProgressPlugin turned up something we didn't expect — one plugin, Terser, was eating 13+ minutes of every build. Webpack wasn't the bottleneck. JavaScript minification was. 🔍 That one number changed the whole plan. We kept Webpack. Replaced Babel with SWC. Swapped Terser for esbuild. Moved CSS optimisation to LightningCSS. Parallelised compilation across cores with thread-loader. ⚙️ Fixed a hidden bottleneck too — AWS CodeArtifact tokens were re-authenticating every 12 hours and quietly busting our Docker cache. Restructured the Dockerfile into layers that actually cache. 🐳 20 minutes to 2 minutes. Cold build ~90% faster. Incremental ~95% faster. Across 20 production apps, 600+ PRs a month, 5,000+ CI runs. ⚡ No migration. No four-week rewrite. No new bundler to debug for the next year. The real lesson wasn't about tooling. It was that we almost skipped measurement and went straight to migration — the industry default. Glad we didn't. 💡 Full breakdown — the Speed Measure Plugin data, the pnpm switch, the exact Dockerfile changes — link in the first comment. 👇 Really proud of what our engineering team pulled off here. Worth the read. 🫡 Zepto Zepto Tech #ZeptoTechXPress #ZeptoEngineering

𝗡𝗮𝗶𝘃𝗲 𝗥𝗔𝗚 𝘄𝗼𝗿𝗸𝘀 𝗶𝗻 𝗮 𝗱𝗲𝗺𝗼. 𝗜𝘁 𝗳𝗮𝗶𝗹𝘀 𝘁𝗵𝗲 𝗺𝗼𝗺𝗲𝗻𝘁 𝗿𝗲𝗮𝗹 𝘂𝘀𝗲𝗿𝘀 𝘀𝗵𝗼𝘄 𝘂𝗽. Embed → retrieve → generate looks clean in a notebook. Real requirements break it: → Questions whose answer is spread across many documents → Industry terms that embeddings get wrong → Bad chunks the pipeline never catches → Answers that live in how things connect, not in any single chunk → PDFs full of tables and images a text-only index cannot read These 5 architectures are how serious teams stay ahead in the agentic AI era: 𝟬𝟭 𝗛𝘆𝗯𝗿𝗶𝗱 𝗥𝗔𝗚 → Dense vectors find meaning. BM25 finds exact words. → Reciprocal Rank Fusion combines both ranked lists. → A safe baseline for almost every team. 𝟬𝟮 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚 → Pull entities and their relationships into a knowledge graph. → Retrieve subgraphs and community summaries, not chunks. → Best when the answer lives in how things connect. 𝟬𝟯 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 → A planner agent picks the right tool: vector, web, or SQL. → A reasoner agent keeps trying until the answer is solid. → Retrieval becomes a plan, not a single step. 𝟬𝟰 𝗖𝗼𝗿𝗿𝗲𝗰𝘁𝗶𝘃𝗲 𝗥𝗔𝗚 (𝗖𝗥𝗔𝗚) → Grade every retrieval before you trust it. → Correct → answer. Unclear → rewrite the query. Wrong → search the web. → This is what production RAG actually looks like. 𝟬𝟱 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗥𝗔𝗚 → One embedding model (CLIP, ColPali) for text, images, and tables. → One vector index. One multimodal LLM. → No more separate pipelines for PDFs with charts. I built a runnable example for each of the five patterns. GitHub link in the first comment. The best teams in 2026 do not pick one. They combine them — hybrid retrieval inside an agentic loop, with a corrective grader, over a multimodal index. Naive RAG is a starting point, not a finish line. That is why most enterprise GenAI projects stall at the demo. Which of these five becomes the default RAG stack in the next 18 months — and which stays a specialized tool?

Only 25% of wealthy families successfully preserve wealth into the second generation. Roughly 10% make it to the third generation, and just 5% sustain that wealth into the fourth. Those numbers help explain why many Family Offices are being forced to rethink their structure, priorities, and long term purpose. The traditional image of the Family Office has long been tied to scale, exclusivity, and large internal operations. Dedicated investment teams, private legal counsel, concierge services, and layered governance structures became markers of sophistication for ultra wealthy families seeking greater control over their financial lives. Now, many Family Offices are moving in a different direction. Despite continued growth in global wealth, a rising number of Family Offices are downsizing, consolidating operations, or shutting down entirely. The shift has less to do with declining wealth and more to do with rising complexity, operational costs, and changing generational priorities. Maintaining a fully staffed Family Office today requires significant expense across talent, compliance, cybersecurity, technology, and administration. For many families, especially those below the ultra large institutional level, the structure no longer delivers the efficiency it once promised. The issue is rarely investment performance alone. More often, wealth disappears because of weak governance, lack of communication, succession failures, entitlement, and growing family fragmentation over time. Generational transition is also reshaping the Family Office itself. Second and third generation family members often bring different investment philosophies, levels of involvement, and long term priorities. As families spread across multiple regions and jurisdictions, alignment becomes more difficult and governance grows more complicated. In response, many families are adopting leaner structures focused on oversight and strategy while outsourcing specialized functions to external partners. Investment management, estate planning, reporting, cybersecurity, and administrative services can now be handled externally with institutional quality support. Technology has accelerated this shift, allowing smaller teams to operate with greater efficiency and visibility than ever before. The conversation is also becoming more intentional. Many families are no longer measuring success by the size of their operation. Instead, the focus has shifted toward governance, communication, succession planning, and long term family cohesion. In many cases, a smaller and more focused Family Office structure may be better suited for preserving wealth across generations than a large internal organization weighed down by complexity. The Family Office industry is still growing globally, but the model itself is changing. The future Family Office will likely be defined less by size and more by adaptability, clarity, and strategic coordination.

2026 will be an unprecedented year for consumer electronics, and the smartphone industry in particular. For fifteen years, the smartphone industry relied on a single, reliable assumption: components would inevitably get cheaper. While short-term volatility existed, the long-term downward trend in memory and display costs allowed for annual spec bumps without price hikes. In 2026, that model has finally broken, driven by a sharp and unprecedented surge in memory costs. AI has fundamentally reshaped demand. The same memory used in smartphones is now critical for AI data centers, as hyperscalers lock in silicon wafer capacity years in advance to fuel the AI boom. For the first time, smartphones are competing directly with AI infrastructure and memory prices are rising sharply as a result. In some cases, memory costs have already increased by up to 3x, with further rises expected as unprecedented demand continues to swallow available supply. Memory is fast becoming one of the most expensive smartphone components and potentially the single largest cost driver in the bill of materials by year-end, with estimates suggesting that memory modules which cost less than $20 a year ago could exceed $100 by year-end for top-tier models. The result is a structural shift. This is a reversal of everything we’ve come to expect from this industry. When something that used to get cheaper every year suddenly becomes a lot more expensive, the economics of building a smartphone fundamentally change. Brands now face a simple choice: raise prices, by 30% or more in some cases, or downgrade specs. The “more specs for less money” model that many value brands were built on is no longer sustainable in 2026. As a result, some markets, particularly entry and mid-tier segments, are likely to shrink by 20% or more, and brands that have historically dominated these segments will struggle. Pricing will inevitably also increase across our smartphone portfolio, particularly as we will upgrade some products launching this Q1 to UFS 3.1. However, for Nothing, the current situation represents a great opportunity. Operating without the cost advantages of industry giants forced us to innovate differently. We learned early on that we couldn’t win on spec sheets alone; instead, we focused on perfecting the user experience, proving that how a phone looks and feels matters far more than its raw numbers. That’s where our focus has always been. 2026 is the year the "specs race" ends. As the industry resets, experience becomes the only real differentiator. That is exactly what Nothing was built for. The era of cheap silicon is over. The era of intentional design is just beginning.