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Martin Fowler's May 27 Fragments brings together four arguments with direct implications for teams working with AI agents. All four are worth covering. Ian Johnson: build quality gates before releasing the agent Ian Johnson published a series about restructuring a gnarly codebase: three months, 258 commits, moving from a Laravel monolith with no tests to an application with automated quality gates and an AI agent shipping production code with minimal supervision. The insight Fowler highlights is about the transition from in-the-loop to on-the-loop: "For the first two months of this project, I used Claude Code with auto-approve turned off. Every file edit, every terminal command, every change… I reviewed it before it executed. The results were good. The code was clean. But I was doing most of the thinking and half the typing. The agent was a fancy autocomplete with better suggestions." Ian Johnson Manual review of every change is not how you build trust in the agent. Trust comes from building the structure that ensures the agent will do the right thing, then stepping back. The sequence: characterization tests first, static analysis, architectural patterns that make things flow correctly. Fowler notes this is exactly the sequence he would use himself. Adam Tornhill: roughly 2 hours of cognitive endurance Adam Tornhill observes that agentic work has a decision density that is mentally more expensive than it appears. The estimate is roughly two hours as a sustainable limit, not a full day of work. The implication: adding more parallel agents does not solve the problem, because the bottleneck is the coordinating engineer's cognitive capacity, not available processing volume. The solutions are smaller tasks, automation, and verification mechanisms, not more parallelism. NHS: closing open source repositories NHS (UK National Health Service) closed open source repositories citing LLM threats to code security. The UK Government Data Services countered directly: making code p

AI image tools rarely make me feel like I'm part of the creative process. They are, afterall, mostly designed so that people with no design experience can type in a few words and get back a usable result. So I was pleasantly surprised by Adobe's latest take on an AI image assistant: it's a bot […]

One unified API for Gemini, OpenAI, Anthropic, HuggingFace, Ollama, on-device Gemma, and GGUF models — with tool calling, memory, and safety guardrails built in. The Problem Building AI agents in Flutter is fragmented. Every provider has a different API shape. There's no standard way to switch between cloud and on-device inference. Tool calling, persistent memory, and safety guardrails are always custom implementations. The result: developers rebuild the same plumbing for every project. What It Is genesis_ai_sdk is a universal Flutter SDK for building AI agents that run locally and in the cloud. One clean API. Seven providers. Zero vendor lock-in. Supports: Gemini (Google) OpenAI (GPT-4o) Anthropic (Claude) HuggingFace (any public model, no download needed) Ollama (local server, no API key) On-device Gemma (fully offline) On-device GGUF via llama.cpp (fully offline) Switch providers by changing one line. Your agent code stays the same. Quick Start — 10 Lines of Code import 'package:genesis_ai_sdk/genesis_ai_sdk.dart' ; final agent = GenesisAgent ( provider: GeminiProvider ( apiKey: 'YOUR_KEY' ), systemPrompt: 'You are a helpful assistant.' , tools: [ GenesisTools . calculator , GenesisTools . dateTime ], ); final response = await agent . chat ( 'What is 1337 * 42, and what day is it?' ); print ( response ); The agent figures out which tool to call, executes it, and returns the answer. No prompt engineering needed. The Features That Actually Matter Real Tool Calling — Not Just Text The ReAct loop is fully implemented. The agent reasons → calls tools → observes results → repeats until it has a complete answer. An onStep callback fires for every intermediate step — perfect for building a "thinking…" UI. Custom tools are five lines: final weatherTool = GenesisTool . define ( name: 'get_weather' , description: 'Returns weather for a city.' , params: { 'city' : ToolParam . string ( description: 'City name' )}, execute: ( args ) async = > fetchWeather ( args [ 'city' ]), )

GitHub reports cutting token costs in agentic CI workflows by up to 62% by pruning unused MCP tools, swapping some MCP calls for gh CLI, and running daily “auditor” and “optimizer” agents. A token-usage.jsonl artefact and an Effective Tokens metric help track spend across models and spot regressions. By Mark Silvester

Every line of your smart contract costs something. Some lines cost more than others. In this part of our gas saving series, we’ll explore how to write smarter Solidity code that keeps your contract lean and efficient. Here are six simple and practical ways to reduce gas costs while writing Solidity smart contracts. 1. Use payable Only When Needed, But Know It Saves Gas In Solidity, a function marked payable can actually use slightly less gas than a non-payable one. Even if you're not sending ETH, the EVM skips some internal checks when the function is marked payable. See this example: function hello() external payable {} // 21,137 gas function hello2() external {} // 21,161 gas That tiny difference may not seem like much, but across thousands of calls, it adds up. Only use payable when your function is actually meant to accept ETH 2. Use unchecked for Safe Arithmetic When You’re Sure Since Solidity 0.8.0, all arithmetic operations automatically check for overflows and underflows. While this makes contracts safer, it also uses extra gas. When you're certain that overflow won't occur, you can use the unchecked keyword to skip these safety checks. uint256 public myNumber = 0; function increment() external { unchecked { myNumber++; } } Gas used: 24,347 (much cheaper than using safe math) Warning: Use unchecked carefully. Only when you're confident there's no risk of overflow. 3. Turn On the Solidity Optimizer The Solidity Optimizer is like a smart helper that cleans up and tightens your compiled bytecode. It does not change how your contract works, but it removes waste and makes it cheaper to run. If you’re using tools like Hardhat or Remix, always enable the Optimizer before deploying to mainnet. 4. Use uint256 Instead of Smaller Integers (Most of the Time) Smaller types like uint8 or uint16 might look more efficient, but they can cost more gas during execution. That’s because the EVM automatically converts them to uint256 behind the scenes. So, if you're not tightly p

Agentic AI coding tools are transforming how we build software. But they share a fundamental constraint: context windows are finite, and as chat sessions grow, AI performance degrades, a phenomenon Anthropic calls context rot . The model loses its grip on early instructions, leading to a frustrating "fix-it loop" where the agent fixes one thing but breaks another. Most of us prompt an agent, let it write code, review it, and repeat. This works beautifully for prototypes. But when you need to build a stable, full-featured product with hundreds of mission-critical acceptance criteria (AC), "vibe-coding" breaks down. The reality is that you get better behavior from agents the same way you get it from humans, by explicitly capturing what good and bad look like, and checking against it . Coming from a systems engineering background in regulated industries, I knew we needed to stop treating agents like conversational chat buddies and start treating them like engineering assets. That's why I built DevCortex : a purpose-built structured intelligence layer that brings systems engineering discipline to agentic workflows. What is DevCortex? DevCortex is an agentic development platform built on one core idea: AI agents work best when they have structured, queryable access to a database of requirements they can interrogate on demand, not a wall of text in a prompt. It sits between the human specification and AI execution using three components: 1. An Agentic-V Model Database: A structured hierarchy mapping your high-level vision (ConOps) to system specs (Specs), individual requirements (Reqs), linked defects (Issues), and an auto-generated Traceability Matrix. 2. An MCP Server: Delivers just-in-time, high-signal context to tools like Claude Code or Open Code. Instead of dumping requirements upfront, the agent queries exactly what it needs, when it needs it. 3. Human Control Planes (Web UI & CLI): A multi-user Web UI with real-time WebSocket feeds to watch your agent work, plus a

Over 51% of all GitHub commits in early 2026 are AI-generated or AI-assisted. That statistic creates a problem no one anticipated when AI coding tools first launched: who reviews the AI's code? The answer, increasingly, is another AI. The AI code review market has grown rapidly alongside vibe coding and AI-first development workflows. But the category is fragmented there are PR-level reviewers, IDE inline analyzers, security scanners, and general-purpose AI assistants all claiming to do "code review." They work very differently, and picking the wrong one for your workflow is a real productivity cost. This guide cuts through the noise. We explain what each category does, highlight the best tools in each, and give you a decision framework to help you choose what fits your actual situation. Why AI Code Review Is Now Essential Three converging trends make AI code review the category to watch in 2026: AI-generated code has real quality problems. Research shows 45% of AI-generated code fails at least one OWASP Top 10 security check, and 53% of developers have found security vulnerabilities in AI-written code. When you use tools like Cursor, Claude Code, or GitHub Copilot to write 80% of a feature, you're shipping code you may not have read line by line. Code review is a bottleneck. Stack Overflow's 2026 developer survey found code review wait time is the top-ranked productivity killer. For solo developers and small teams, reviews pile up and slow shipping. AI reviewers don't have calendars. The security stakes are rising. As more non-developers ship production code via vibe coding, the need for automated security checks compounds. AI review tools catch issues like SQL injection, CORS misconfigurations, and hardcoded secrets before they ship. Two Categories of AI Code Review Before picking a tool, understand that "AI code review" means two distinct things. 1. PR-Level AI Reviewers These run at the pull request level. When you open a PR on GitHub, GitLab, or Bitbucket, they

In the previous article , we started with a brief introduction to tensors . In this article, we will explore why tensors are useful . Why Tensors Matter Unlike normal scalars, arrays, matrices, and multi-dimensional matrices, tensors are designed to take advantage of hardware acceleration . Tensors do not just store data in different shapes. They are also designed to perform mathematical operations on that data efficiently and quickly . Tensors and Hardware Acceleration Tensors can take advantage of GPUs (Graphics Processing Units) , which many of us use in our day-to-day devices. GPUs are very good at performing many mathematical calculations in parallel, making them useful for training neural networks. There is also specialized hardware called TPUs (Tensor Processing Units) . TPUs are specifically designed to work with tensors and help neural networks run even faster. Automatic Differentiation Another important use case of tensors is in backpropagation . In neural networks, we estimate the optimal weights and biases using backpropagation. This process requires calculating many derivatives and applying the chain rule . Instead of manually calculating all these derivatives, tensor frameworks can handle this automatically using something called automatic differentiation . This means that even as neural networks become more complex, tensors help manage the difficult mathematical calculations behind the scenes. So that is it for tensors. In the next article, we will explore another topic AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production. git-lrc fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free. Any feedback or contributors are welcome! It's online, source-available, and ready for anyone to use. Give it a ⭐ star on Github

The moment I realized AI coding assistants were broken I was building a side project — a simple task manager app. I opened Claude Code, typed: "Add user authentication with email and password login" …and hit enter. Twenty minutes later, I had code. A lot of code. Authentication logic, routes, middleware, even some basic tests. But there was a problem. The frontend (me, on a different day) had assumed a different API shape. The tests only covered the happy path. There was no architecture decision to reference — I just picked JWT because it felt right. And the docker-compose.yml ? It didn't exist yet. I had AI-generated code, but no real software development workflow. What was actually missing Good software isn't just code. Before you write a single line, you need: A spec that everyone (including future-you) agrees on An architecture decision that explains the why Backend and frontend designed to talk to each other Tests that prove things actually work A code review that catches security holes before they ship A deployment config that someone can actually run Normally, a team handles all of this. A PM writes the spec. An architect proposes options. Engineers implement and review each other's work. A DevOps person sets up CI/CD. What if AI could fill all those roles? Building the pipeline I built claude-dev-pipeline — a Claude Code plugin that orchestrates a team of specialized AI agents, each with a specific job. airwaves778899 / claude-dev-pipeline 7-agent full-stack development pipeline plugin for Claude Code — PM → Architect → Backend → Frontend → QA → Reviewer → DevOps claude-dev-pipeline A Claude Code plugin that orchestrates 7 specialized AI agents to take your feature request all the way from requirements analysis to production deployment — with a human-in-the-loop checkpoint at every phase. 中文說明 Why? Writing a feature involves more than just code. You need: A clear spec that everyone agrees on An architecture decision before you write a single line Backend and

Hey there! If you've been keeping up with the AI space lately, you know we're in the middle of something genuinely historic. What used to be science fiction is becoming production code — and it's happening fast. The Big Shift: Agents Over Assistants For years, we've been building chatbots. Helpful little assistants that answer questions. But something changed in 2026, and honestly, it happened so quietly that most people missed it. Agents aren't chatbots. A chatbot waits for you to ask. An agent sees an objective and acts on it. Autonomously. That's the difference. And the market just woke up to it. What's Actually Happening Right Now DBS Bank + Visa's Agentic Commerce Tests In February, these giants quietly completed trials of AI-driven agents executing credit card transactions automatically. No human in the loop. No confirmation needed. Just agents doing their job. If you're thinking "That sounds risky" — yeah. But it worked. BridgeWise's AI Wealth Agent A US fintech company just unveiled an AI agent that personalizes investment portfolios at scale . Something that would take a team of human financial advisors years to do, this agent does in minutes. Microsoft's Supply Chain Agents They're operating over 100 AI agents in their own supply chain. And they're planning to equip every employee with AI support by end of 2026. The Emergence of "Freelance Agentics" This one's wild. Solopreneurs are using AI agents to do the work of 10-person teams. Legal, accounting, architecture — fields that were supposedly "too complex" for automation are getting flipped upside down by a single person + a good agent framework. Why This Matters for Developers Here's what I think is important: This isn't hype. These are real companies running real agents in production. If you're a developer in 2026 and you don't understand how to build with agents, you're going to feel left behind. Not because everyone's obsessed with them — but because they're genuinely useful . The frameworks are solid

"The output filter runs after the LLM has already seen the confidential data. By then, three classes of leak can no longer be stopped. The right surface is retrieval. Walking through a real implementation." TL;DR Most RAG-with-RBAC stacks I see in production put the access-control gate at the output stage: an LLM-response post-filter that masks PII or redacts confidential strings. This is defense-in-depth, not the load-bearing layer. By the time the filter runs, the LLM has already received the confidential context, and three classes of leak — creative paraphrasing, inference, cross-turn persistence — can no longer be stopped by string-matching the output. The protective surface that actually carries the weight is retrieval-stage ABAC: documents and graph nodes the user can't read are never traversed, never make it into the prompt, never seen by the model. The output filter still belongs in the stack, but as the second-to-last line, not the first. This post is a walk through why and how, with code references from a working implementation. It was prompted by a 6-turn LinkedIn DM exchange with Ali Afana (Provia founder, dev.to Featured) on injection-fixture schema design, where the framing crystallized. The seductive default You build a RAG system. You have documents at different sensitivity levels — public, internal, confidential. You want the model to answer based on whichever documents the user is allowed to see. The default mental model: "I'll let the model answer freely, and then I'll filter the response on the way out." This is appealing because: The retrieval pipeline stays simple (one query, one vector search, one response) The access control feels surgical (just before the user, just before damage) The PII-mask vocabulary is well-established (Presidio, regex catalogs, named-entity recognition models) So you wire up something like: Python The seductive default def answer(query, user): chunks = retrieve(query, top_k=10) # No ABAC here context = "\n".join(c.text

The best AI tools for conversion rate optimization (CRO) in 2026 are the platforms that continuously...

At a previous job I worked at as a Dev we had someone who ran Semgrep on our codebase for the first time. It came back with 180 findings. We had no security engineer. The developer who ran it looked at the output, closed the terminal, and we never ran it again. That's not a story about a careless team. That's a story about a tool that produced output most teams in small companies with no expertise knew what to do with. I've seen this exact moment happen more times than I can count working with small dev teams. And it's the reason I spent the last year building SecOpsium. But before I get to that let me explain the actual problem, because I think it's misunderstood. The problem isn't the scanning Semgrep and Gitleaks are excellent tools. They're free, actively maintained, and genuinely powerful. If you're not using them you should be. The problem is what happens 10 minutes after you run them. You get 200 findings. Some are critical. Some are test files. Some are commented-out code from 2021. Some are legitimate secrets. Some are variable names that pattern match against a rule but contain nothing sensitive. They all look the same in the output. Now you're a developer who also does DevOps, also reviews PRs, also handles incidents, and you're staring at 200 items with no clear indication of which three actually matter this week. So you close the terminal. Or you create a Jira ticket labeled "security findings" that lives in the backlog forever. Or you spend two days triaging manually and burn out before you fix anything. This is the real problem. The scanning was never the hard part. Why rule based scanners produce so much noise It helps to understand technically why this happens. Semgrep and Gitleaks are rule-based. They match patterns. A variable named api_key_example in a test file flags the same way as a live Stripe key in an active production config. Gitleaks scans for entropy and known credential patterns but can't distinguish between a key that was rotated and r

The enterprise AI search startup tripled its annual revenue even as tech giants entered the category.

The most downloaded and widely used methodology in the Claude Code ecosystem is Superpowers , with over 208k stars⭐️ and 18.5k forks🍴 on GitHub (and counting!) Going from raw Claude Code to using Superpowers is as revolutionary as going from a manual transmission to an automatic. Here's why this analogy holds: Planning Mode vs. Coding Mode When we build with Claude (or Codex, or Gemini...), we're usually in one of two states: planning mode coding mode Like shifting gears in a car🚘, moving between these modes effectively is what makes it all work. Skip the shift, and you're just revving your engine (aka burning context and going nowhere.) Superpowers lets Claude automatically detect and shift between these modes. It picks up on ambiguity in your prompt and ensures it fully understands the task before entering coding mode. No manual shifting required! Slower, but for Good Reason Superpowers can feel slower and less satisfying than raw Claude Code, like how driving an automatic can feel less engaging than a manual. But both serve the same critical purpose: they stop you from blowing up your engine! That said, there's still a time and place for raw Claude. Sometimes you want direct, fast, unmediated output. But that minor gain in speed often isn't worth the added complexity, the ambiguity risk, and the tradeoffs that come with it. The Layer Underneath Entire is building the foundation beneath all of this — capturing every session and decision, and linking it to the commit it produced. It's the full record of how the code was written, not just the code itself. So the question is this: how will you put your agent in drive🚘?

I had a screenshot to send. Nothing secret — a stack trace from a side project — but it had an internal hostname, a file path with my username, and a chunk of a config file in the terminal behind it. The fast move is to drag it onto a free image host and paste the link. I sat there with my cursor over the upload button and couldn't do it. Because I know what happens next. That image lives on someone else's infrastructure, indefinitely, behind a URL I don't control, and I have no idea who else can reach it. For a throwaway screenshot, that's a permanent record I never agreed to. So I closed the tab and built a thing instead. It's called tmpdrop , and it's ~500 lines of Node. The threat model The problem with public file hosts isn't that they're evil. It's the gap between what you intend ("share this once, with one person") and what the platform delivers ("store this forever, serve it to anyone who finds the link"). A few specific things go wrong: Retention. "Temporary" hosts keep your files long after you've forgotten them. There's no expiry you can trust, and deletion is usually best-effort. Predictable URLs. Plenty of hosts use sequential or short IDs. Scrapers walk the keyspace and hoover up everything. Your "private" link was never private. Stored XSS via uploads. If a host serves an uploaded .html or .svg file inline with a permissive content type, an attacker can ship JavaScript that runs in your browser, in the host's origin. Your file host becomes an XSS delivery service. Abuse vectors. No rate limit means the box is a free CDN for whatever someone wants to dump on it — malware, spam payloads, the works. So the design goal wasn't "another uploader." It was: close each of those gaps, then stop. What I built tmpdrop is a single Fastify server backed by SQLite. The whole defensive surface is small enough to hold in your head: Unguessable URLs. Slugs are 9 random bytes, base64url-encoded — 72 bits of entropy. You cannot enumerate them. A TTL reaper. Every upload

Everyone can build it. Almost no one can afford to run it at scale. And the companies selling the picks and shovels are about to get undercut by the same forces they unleashed. by VEKTOR Memory — 20 min read How This Article Started: 20 Forums, 40 Headlines, and a Growing Sense That Everyone Was Confused I woke up to clear skies and the sun finally shining, and I set out to understand this idea, the truth behind it, and the nagging suspicion that the narrative around AI and software costs had become so loud, so uniform, and so confidently confusing that someone needed to sit down and actually go through it. No tweets, or are they now X's? No LinkedIn thought leader infomercials, no Substack hype, just actual research and deep thoughts. So I spent time reading, collating data. Forums, whitepapers, LinkedIn posts, Hacker News threads, VC essays, Reddit arguments. I went looking for the real signal underneath the noise. What I found instead was the full spectrum of human overconfidence, lots of moat real estate. On one end: the hype machine at full throttle. “Software is going to zero.” “A solo dev can now build what a 50-person team built in 2021.” “The era of the $500/month SaaS subscription is over.” “Vibe coding will replace your entire engineering org.” These headlines were everywhere. Breathless. Confident. Shared tens of thousands of times, this angle gets views, of course, the algorithm loves being fed claps, shares, comments, and reposts. Most were written by people who had a very good Tuesday with Codex, Windsurf, Claude and Cursor and decided that instant dev, open source to Github and getting oodles of stars, maybe even roping in a celebrity, was now the permanent condition of software development. “We are now famous on GitHub!" Very hipster, very vibes, see you on the playa.. On the other end: the backlash. Experienced engineer, people with 15 to 25 years in production systems are pushing back hard. “Show me the vibe-coded app that survived its first real

I Built a Local AI Agent That Thinks Like a Brain, Not a Database Most AI agents today are sophisticated autocomplete engines. Ask them something, they answer. Ask again in a new conversation, they start from zero. The context window is the only memory they have. Serenity is different. It's a fully local AI agent that encodes experiences the way biological brains do — semantically clustered, causally structured, and self-organizing. No cloud. No API calls to a vector database. No data leaves your machine. Ever. The Core Problem with Current AI Memory The standard approach to AI memory is essentially a hack: you stuff embeddings into a vector DB, do nearest-neighbor retrieval, and dump the results into the prompt. It sort of works. But it's not how brains work. Your brain doesn't search for memories. When one fires, related ones light up automatically. Serenity's architecture — called S.E.R.A (Semantic Experience Reasoning Agent) — tries to bridge that gap. Here's the key difference: Traditional Approach Serenity Vector search on embeddings Semantic node activation Prompt-injected context Persistent working memory One-shot retrieval Emergent recall via association Static embeddings Pruned & crystallized over time How It Works: The Neural Node Network At the core is the Neural Node Network (NNN) . Instead of storing facts in isolation, Serenity encodes experiences in causal format: ACTION → BEFORE → OUTCOME → AFTER When she learns something, she doesn't file it in a folder. She finds where it semantically belongs in a web of related concepts. Similar things cluster together — the same way neurons that fire together wire together. Then the abstraction layer kicks in. Three or more related concepts crystallize into a higher-order node: the thing they all have in common that none of them says directly. Those nodes bundle into pathways. Those pathways grow into domains. She also has inhibitors and pruning — weak connections get cut so strong ones sharpen. Her knowledge ge

Every token your agent spends on file I/O is wasted reasoning capacity. I was building a document processing agent — the kind that reads incoming research reports, extracts key findings, and produces executive briefings. Nothing exotic. The kind of workflow thousands of teams are automating right now. The PDF I was testing with was 2MB. Dense text. A typical industry research report. When I measured the token cost of processing it inline, the number was 97,354 input tokens — just to get the text into Claude's context. At claude-sonnet-4-6 pricing, that's $0.29 per document. For a pipeline that processes 500 reports a month, you're looking at $150/month before your agent writes a single word of output. That's the problem nobody talks about in the AI agent space. Everyone optimises prompt engineering and output tokens. The silent cost is input: the files, the content, the raw data you're shoving into context before the agent can do anything useful. How the token count explodes When you pass a document to an agent inline, one of two things happens: Option A — Base64 encoding. You read the binary file, encode it, embed it in the prompt. A 2MB PDF in base64 is ~2.7MB of text. At roughly 3.5 characters per token, that's ~770,000 tokens before your agent has read a single word. This is catastrophic. Don't do this. Option B — Text extraction. You extract the raw text content first (via pdftotext , PyMuPDF, or equivalent), then pass the text to the agent. Better — but a 2MB PDF with dense content still yields ~97,000 tokens of extracted text. You've paid for every word, every header, every footnote. Either way, the document content dominates your context window, crowds out your system prompt, and you're burning money on file I/O instead of reasoning. The alternative: specialist services via MCP Model Context Protocol (MCP) is Anthropic's open standard for connecting AI agents to external tools and services. The key insight is simple: your agent doesn't need to contain the co

RAG SOTA, Agent Harnessing, and Langfuse Observability for AI Frameworks Today's Highlights Today's top stories delve into optimizing RAG performance with open-source benchmarks, designing robust AI agent systems, and implementing best practices for LLM observability in production. RAG SOTA: I Tested 7 Pipelines and Built SEQUOIA (Open Source) (Dev.to Top) Source: https://dev.to/__2ddbae6bb7d/--5cec This article presents a comprehensive benchmark of seven Retrieval-Augmented Generation (RAG) pipelines, culminating in the development and open-sourcing of SEQUOIA, a new RAG system. The author details over 20 hours of compute time spent locally to rigorously test different RAG configurations against real-world tasks, providing valuable insights into their performance characteristics. The technical deep dive includes discussions on various components like chunking strategies, embedding models, vector databases, and re-rankers, along with their impact on retrieval quality and generation coherence. Readers gain an understanding of the trade-offs involved in designing effective RAG systems and the empirical evidence supporting different architectural choices. The release of SEQUOIA as an open-source project means developers can directly implement and experiment with a battle-tested RAG pipeline, offering a tangible starting point for their own projects. Comment: This is an invaluable resource for anyone building RAG. Benchmarking 7 pipelines and open-sourcing a well-performing one provides immediate practical value and a solid foundation for further experimentation. Stop Upgrading the Model. Start Engineering the Harness. (Dev.to Top) Source: https://dev.to/tacoda/stop-upgrading-the-model-start-engineering-the-harness-194 This insightful article argues that instead of solely focusing on larger or "better" base models, teams should invest in "engineering the harness" around their AI agents to improve performance. The author highlights that the supporting architecture—compri