标签：#orm

TL;DR — The DIFP protocol was designed to be data-compact and geo-aware from day one. We recently discovered it maps almost perfectly onto the Nostr event format. Here's how, and why it matters for decentralized food infrastructure. Background: What Is DIFP? DIFP (Djowda Interconnected Food Protocol) is an open protocol designed to sync food product data across distributed nodes — compactly, efficiently, and with geo-location awareness built in by default. One of its core design decisions is the PAD system (Preloaded Asset Distribution): Apps ship with a preloaded asset pack — item metadata, compressed images, category structure — all bundled at install time. Only price and availability need to travel over the wire during sync. This means the data footprint per product is tiny. Very tiny. Enter Nostr Nostr is a simple, open protocol for decentralized communication. One of its key specs: events support up to 64 KB of content . When we started exploring Nostr as a potential transport layer, we ran the numbers — and the fit was surprisingly clean. The Math: Products Per Event Baseline encoding A product represented with three fields: { "id" : 500 , "available" : true , "price" : 30000 } At this level of verbosity, a single 64 KB Nostr event can hold approximately: ~1,500 – 2,000 products Already useful. But we can do better. Optimized encoding Two key optimizations: 1. Drop the availability key — If a product entry exists in the JSON, it's available. If it's absent, it's not. No boolean needed. 2. Drop the field names — Instead of {"id": 500, "price": 30000} , just store: 500,30000 Field mapping is handled at the app level, not the protocol level. The device knows position 0 is the product ID, position 1 is the price (in smallest currency unit, e.g. cents). Result ~6,000 – 7,000 products per single Nostr event Possibly more, depending on the price distribution and ID ranges in a given catalog. Geo-Discovery: MinMax99 Cells DIFP uses a geo-cell system called MinMax99 to

In the end, the three companies involved all point the finger at each other.

The Uncomfortable Truth Here's a test: when your deployment fails in production, what happens to your main branch? If the answer is "the broken code is already merged" — congratulations, you're doing CI/CD with a Git trigger. That's not GitOps. It's a pipeline that happens to watch a branch. I've spent years building platform engineering systems at enterprise scale — identity management frameworks, infrastructure-as-code pipelines, AI agent platforms that manage operational code. And I keep seeing the same mistake: teams adopt "GitOps" by adding a deployment step after merge, then wonder why they get drift. True GitOps has one non-negotiable rule: main always equals production. If a deployment fails, main doesn't change. Period. This isn't just my opinion — it's the logical extension of OpenGitOps principles : declarative desired state, versioned in Git, automatically reconciled. The enforcement mechanism I'm describing is how you make those principles real rather than aspirational. The Anti-Pattern Everyone Runs The most common "GitOps" setup I see in enterprise teams looks like this: Developer opens PR CI runs tests Reviewer approves PR merges to main Deployment triggers from main ❌ Deployment fails main now contains code that isn't in production This is merge-then-deploy . It's standard CI/CD with extra steps. The moment you merge before confirming a successful deployment, you've broken the core GitOps contract: Git as the single source of truth for what's actually running. The result? Drift. Stale state in main . A branch that lies about what's deployed. Every subsequent PR is now based on a broken foundation. The Enforcement Pattern: Deploy Before Merge The fix isn't philosophical — it's mechanical. GitHub's Merge Queue gives you exactly the right primitive: Developer opens PR CI runs tests (standard checks) Reviewer approves → PR enters the merge queue Merge queue trigger runs a dry-run deployment against the target environment If dry-run passes → queue trigge

Managing a multi-million product catalog on Magento presents unique challenges around performance, scalability, and operational efficiency. At Rave Digital, we recently undertook a Magento performance optimization project for a large-scale eCommerce merchant struggling with slow site speed, infrastructure bottlenecks, and backend instability. This use case breakdown details how we modernized their Magento architecture, optimized database performance, and scaled infrastructure to deliver a stable, high-speed shopping experience. This post is tailored for eCommerce managers, directors, and Magento merchants—especially those running Adobe Commerce or Magento Open Source platforms—who want to understand practical strategies for Magento architecture scaling and performance tuning for large catalogs. The Problem: Performance Bottlenecks in a Complex Magento Environment: Our client operated an enterprise Magento store with a multi-million product catalog. Despite Magento’s robust capabilities, the site suffered from: Slow page load times impacting user experience and SEO Scalability challenges as product volume and traffic grew Infrastructure bottlenecks causing backend instability and downtime Complex integrations and manual processes limiting operational efficiency Platform limitations in handling large catalog management and real-time inventory updates These issues collectively threatened the site’s ability to support growth and deliver a seamless customer experience. The client sought a comprehensive Magento platform modernization to address these challenges. Context: Why Magento Architecture and Infrastructure Matter Magento’s flexibility and extensibility make it ideal for enterprise eCommerce, but large catalogs require careful architecture and infrastructure planning. Key technical pain points include: Database performance under heavy read/write loads Indexing delays and cache invalidation impacting site speed Integration complexity with third-party systems and API

TypeORM 1.0 is the first major release of the open-source TypeScript and JavaScript ORM since its inception in 2016. This version modernizes platform requirements, removes deprecated APIs, and introduces numerous bug fixes and new features. TypeORM now supports ECMAScript 2023, dropping older Node.js versions and dependencies while enhancing security and migration processes. By Daniel Curtis

To provide SRE as a service, a team built a center of excellence, introducing Federated SREs and roles like production manager and technical tribe lead. They created a culture of data-driven conversations where SLOs and SLAs were democratised. Surviving growing cognitive load meant continuously simplifying architecture and embedding sovereignty and resilience into platform design decisions. By Ben Linders

The speakers discuss the architectural challenges of executing safe data deletion across distributed datastores. Balancing durability, availability & correctness, they explain how to orchestrate multi-system deletion propagation without impacting live traffic. They share lessons on controlling tombstone accumulation, building continuous audit loops, and gaining trust with a centralized platform. By Vidhya Arvind, Shawn Liu

This is part thirteen in a series about managing the growing pile of skills, scripts, and context that AI coding agents depend on. Part ten covered the full improve pipeline — all five phases and how they connect. Part fourteen covers what 48 runs per day looks like in practice, including hardware benchmarks and the reliability bugs that surface at that frequency. The reflect pass inside akm improve has three execution modes. Most installs are still running the slowest one. Agent mode — the original — spawns an opencode or claude subprocess for each reflect call. The subprocess starts cold, acquires a session, assembles context, makes its LLM call, and exits. That cold-start overhead is real: each call takes approximately 30 seconds on a quiet machine. Run akm improve against a 69-ref stash and the reflect phase alone costs about 35 minutes. SDK mode eliminated the subprocess. The reflect call runs in-process, cutting per-call latency to 10–15 seconds. A 69-ref run drops to 12–17 minutes — better, but still bounded by round-trip overhead that the reflect task does not actually need. LLM mode removes the round trip entirely. The context for reflect is statically pre-assembled — no live tool calls, no file reads, no external context needed. A direct HTTP call to the LLM endpoint is sufficient, and it costs 6–10 seconds per call. A 69-ref run completes in 8–10 minutes. Mode Per-call latency 69-ref run agent (CLI subprocess) ~30s ~35 min sdk (in-process) ~10–15s ~12–17 min llm (direct HTTP) ~6–10s ~8–10 min The 3–4× end-to-end improvement is from eliminating overhead that was never necessary for what reflect does. Why Reflect Does Not Need an Agent The reflect pass takes a stash asset, examines its current content, and proposes a refined version. The inputs are fixed before the pass starts: the asset text, its metadata, and the improvement prompt. Nothing changes mid-call. No files need to be opened. No search queries need to fire. No external context needs to be pulled

If confirmed, it would be the fly's first breach of the US-Mexico border.

Google has shared details of its fleet wide large scale A/B experimentation system designed to standardize experiment assignment, exposure logging, and configuration propagation across distributed services. The approach enables consistent measurement across products, reduces experiment conflicts, and improves reliability of data driven decision making at scale. By Leela Kumili

There's a specific feeling you get after your third production caching incident. It's not panic....

Your API is fast. Your code is clean. Your architecture looks solid on paper. Then you hit 500,000 records and everything slows down. Queries that ran in 12ms now take 4 seconds. Your dashboards lag. Users start filing support tickets. Your on-call engineer is staring at a query plan at midnight wondering what went wrong. Nine times out of ten, the answer is indexing. Not missing indexes — wrong indexes. Indexes that exist but don't help. Indexes that actively hurt write performance without meaningfully improving reads. This is a breakdown of the most damaging database indexing mistakes in production SaaS systems — and how to fix them before they become incidents. Mistake 1: Indexing Everything "Just in Case" The most common mistake isn't under-indexing. It's over-indexing out of anxiety. New engineers especially fall into this pattern — add an index on every column that appears in a WHERE clause, just to be safe. Seems responsible. It isn't. Every index you add is a write tax. On every INSERT, UPDATE, and DELETE, PostgreSQL (or MySQL) has to update every index on that table. On a table with 8 indexes, every write touches 8 data structures. At low volume, this is invisible. At 10,000 writes per minute, it becomes your bottleneck. The fix: Audit your indexes regularly. In PostgreSQL: SELECT schemaname , tablename , indexname , idx_scan , idx_tup_read , idx_tup_fetch FROM pg_stat_user_indexes ORDER BY idx_scan ASC ; Any index with idx_scan = 0 or near zero hasn't been used since your last stats reset. That's a candidate for removal — not immediately, but after investigation. Mistake 2: Not Understanding Index Selectivity An index on a boolean column ( is_active , is_deleted ) is almost always useless. Here's why: selectivity measures how many distinct values exist relative to total rows. A boolean column has two values. If 95% of your rows have is_active = true , an index on that column tells the query planner almost nothing useful. It will often skip the index entire

This week's Java roundup for May 25th, 2026, features news highlighting: lifecycle changes with two of the JEPs that were targeted for JDK 27; the GA release of Koog 1.0; point releases of Hazelcast, Quarkus, Hibernate and JHipster; the eighth milestone release of Spring AI 2.0; and introducing Endive, a JVM-native WebAssembly (Wasm) runtime. By Michael Redlich

The Instagram accounts for the Obama White House and the Chief Master Sergeant of the U.S. Space Force were briefly defaced with pro-Iranian images and messages over the weekend, after instructions began circulating on Telegram showing how to trick Meta's "AI support assistant" bot into resetting account passwords.

This is a follow-up to SynaptoRoute: A Study in Local Semantic Routing . If you haven't read it, the short version is: SynaptoRoute is a zero-token semantic routing engine that classifies user queries into intents using local embeddings instead of LLM API calls. SynaptoRoute v0.3.0: Matching Semantic Router While Scaling to 50,000 Routes What Changed Since v0.2.0 When I published the first post, SynaptoRoute had just shipped dynamic batching and O(1) hot-reload. The throughput numbers were promising, but the accuracy story was incomplete. I had internal benchmarks but no comparison against a widely adopted baseline under identical, reproducible conditions. That gap is now closed. v0.3.0 is live on PyPI: pip install synaptoroute == 0.3.0 The Benchmarking Journey Getting to these numbers took multiple benchmark revisions. Early synthetic datasets produced catastrophic accuracy collapse and initially suggested that both SynaptoRoute and Semantic Router were performing poorly. After deeper investigation, the root cause turned out to be flaws in the dataset generation pipeline rather than limitations of the routing engines themselves. Several rounds of validation, failure analysis, threshold tuning, adversarial testing, and external benchmarking followed. All final results presented in this article come from independent public datasets with strict train/test separation, eliminating dataset leakage and benchmark inflation. That process was valuable because it forced the project to validate assumptions against real-world data instead of relying on synthetic benchmarks. The Benchmark That Actually Matters I evaluated SynaptoRoute against Semantic Router on two standard NLU datasets. Same embedding model ( BAAI/bge-small-en-v1.5 ). Same hardware. Same evaluation script. Same train/test splits loaded from HuggingFace. CLINC150 150 intents spanning 10 domains, plus an out-of-domain class. This is the standard stress test for intent routers. Metric SynaptoRoute Semantic Router

Shopify introduced GraphQL Cardinal, a new execution engine replacing depth-first traversal with breadth-first execution. The redesign improves large-scale GraphQL performance with up to 15x faster field execution, 6x lower GC overhead, and +4s P50 latency gains. It focuses on execution-layer efficiency and batched resolver processing for high-cardinality commerce queries. By Leela Kumili

An LLM-written kernel benchmarked 38% faster on a microbench. Here is what kernel-level validation showed it actually did at runtime. TL;DR Multi-agent LLMs are now writing CUDA kernels (RightNow AI’s AutoKernel, Meta’s KernelEvolve, a multi-agent system claiming 38% speedup on Blackwell). Source-level benchmarks measure clean throughput on a single isolated kernel. They do not measure SM occupancy under co-scheduling, DRAM bandwidth saturation, dispatcher off-CPU during a real serving workload, or NCCL wait correlation with sibling kernels. Kernel-level validation closes that gap: an eBPF trace of the same kernel running under the same workload as production answers all four questions in one capture. The kernel-writing wave Three pieces of work in April surfaced the same pattern: agents generate CUDA kernels, then quote a single throughput number against a baseline. RightNow AI’s AutoKernel (announced Apr 6) – LLM agents iteratively rewrite CUDA kernels for a target metric, claiming substantial speedups on selected microbenchmarks. Meta’s KernelEvolve – similar shape: agents propose kernel variants, rank by throughput, keep the best. Multi-agent system on Blackwell (Apr 29 reports) – claims a 38% speedup on a public kernel benchmark using a coordinated agent setup. All three are real research, all three produce real kernels, and all three report numbers that come from microbenchmarks. The microbench setup is exactly what you want for the optimization loop. It is not what you get in production. What microbenchmarks do not see Run an LLM-generated kernel under nvprof or nsight-compute on an otherwise-idle GPU and the throughput number is real. Put the same kernel in front of a vLLM serving workload and four properties change immediately: SM occupancy under co-scheduling. The kernel that achieves 95% SM occupancy in isolation will achieve 40-50% with three other kernels sharing the same SMs. The optimizer never sees this regime. DRAM bandwidth saturation. A kernel tha

Modern search engines do more than match keywords. When you search for "cozy mystery set in Paris" and get results for "atmospheric detective novel in France" that's vector search at work: documents and queries are converted into lists of numbers, called embeddings, and the search engine finds the documents whose numbers are closest to the query's. Manticore Search supports this natively. Under the hood, it uses a data structure called HNSW: a graph that connects nearby vectors, so it can find nearest neighbors quickly without scanning every document. That makes vector search fast enough to run on millions of documents in milliseconds. But HNSW has an inefficiency. Early in the traversal, almost every distance computation finds a better candidate than the ones already in the result set. As the search goes on, those improvements become rarer, but the algorithm keeps traversing the graph until it exhausts its exploration budget. By that point, the result set has often already converged, and the remaining work does little or nothing to improve it. Early termination fixes this by detecting that point and stopping early. The effect becomes more noticeable as k grows, where k is the number of nearest neighbors the query asks Manticore to return. Returning more neighbors requires more graph exploration, and much of that extra work happens after the result set has already stabilized. That also makes early termination more valuable, because it has more unnecessary work to cut. This gets more pronounced with vector quantization . Quantization compresses stored vectors to save memory, which slightly lowers search precision. To recover it, Manticore uses oversampling : it fetches 3x more candidates than requested, then rescores them using the original full-precision vectors. With the default 3x oversampling, HNSW explores many more candidates per query. Large k values often come from this kind of candidate expansion: an application may ask the vector index for hundreds or thous

Overview PDF text extraction is a common pre-processing step in data pipelines — ingesting research papers, legal documents, or reports before embedding or indexing. Both pypdf and PdfPig are pure managed-code parsers: no native binaries, no OCR, no system PDF renderer. They implement the same PDF specification operations in their respective languages. This makes the benchmark unusually clean: the performance difference is entirely due to language execution speed, not library architecture differences. Benchmark Setup 200 recent arXiv PDFs (mixed technical papers, 5–40 pages each). Tested on subsets of 10, 50, 100, and 200 files. Both libraries extract all text from all pages; output is validated for page-count agreement and character-count agreement within 15% (pypdf and PdfPig decode whitespace and encoding tables slightly differently). Results PDFs Pages Python (pypdf) .NET (PdfPig) Speedup 10 ~120 ~0.9 s ~230 ms 3.9× 50 ~600 ~4.2 s ~810 ms 5.2× 100 ~1,200 ~8.5 s ~1.4 s 6.1× 200 ~2,400 ~17 s ~2.7 s 6.2× The speedup grows slightly with corpus size, suggesting pypdf has a per-document startup cost that compounds as PdfPig's JIT gets warmer. Why PdfPig Is Faster PDF parsing is byte-heavy: every page is a stream of PostScript-like operators (move, show text, set font, etc.). Each operator must be lexed, looked up in a dispatch table, and executed against a graphics state machine. In Python, each operator dispatch is a Python method call — the CPython bytecode interpreter has overhead per call regardless of what the method does. In .NET, the JIT compiles the dispatch loop to native code the first time it runs; subsequent pages pay only the cost of the actual work. Additionally, PdfPig's content-stream parser operates on ReadOnlySpan<byte> — zero-copy slicing through the raw page bytes with no intermediate string allocations. pypdf builds Python string objects for each token. Key Code // PdfPig — zero-copy span-based page extraction public Result Extract ( string path )

Overview PageRank is the canonical graph algorithm. NetworkX implements it in pure Python — its dict-of-dict adjacency representation means every power-iteration step dispatches millions of Python attribute lookups. When the graph has 1.8 million nodes and 28.5 million edges (Wikipedia category hyperlinks), those lookups dominate the runtime. The .NET replacement uses a CSR (Compressed Sparse Row) matrix — two flat int[] arrays for the graph structure — and TensorPrimitives for the SIMD-accelerated normalization step inside each iteration. Benchmark Setup Five SNAP datasets of increasing size: Dataset Nodes Edges wiki-Vote 7,115 103,689 soc-Epinions1 75,879 508,837 web-Stanford 281,903 2,312,497 web-Google 875,713 5,105,039 wiki-topcats 1,791,489 28,511,807 Algorithm: power-iteration PageRank, damping=0.85, tol=1e-6. Both implementations converge to identical top-10 node rankings. Results Dataset Python (NetworkX) .NET (CSR) Speedup wiki-Vote (103k edges) ~0.8 s ~100 ms ~8× soc-Epinions1 (508k edges) ~8 s ~600 ms ~13× web-Stanford (2.3M edges) ~120 s ~5 s ~24× web-Google (5.1M edges) ~5.5 min ~12 s ~28× wiki-topcats (28.5M edges) ~47 min ~60 s ~47× The speedup grows with graph size because NetworkX's Python dispatch cost scales with edge count, while the CSR inner loop is a tight JIT-compiled SIMD pass. Why CSR Beats NetworkX NetworkX represents each node's neighbors as a Python dict. Iterating the adjacency in one power-iteration step means: Calling G.neighbors(node) — a Python method call Iterating a dict — unboxing int keys, chasing heap pointers Accumulating a float into another dict value — another boxing step That happens for every edge, every iteration, roughly 50–80 times to convergence. CSR collapses the graph to two arrays: rowPtr[n+1] (where each node's neighbors start) and colIdx[edges] (the neighbor list). Iterating neighbors of node v is a tight C loop from rowPtr[v] to rowPtr[v+1] . No Python objects, no dict hashing, no pointer chasing. Key Code // P