标签：#search

Study: Land use is the biggest factor in determining decomposition rates of organic cotton underpants.

AWS describes a specification-driven approach for composing flexible data workflows by separating intent from processing logic. Architecture uses declarative specifications, reusable processing capabilities, and validation before execution. AWS reports that the approach can reduce dataset onboarding from weeks to days while supporting traceability, versioning, data classification, and governance. By Leela Kumili

Agents don't search like humans. They issue hundreds of queries per session, need structured extraction over snippet relevance, and care more about p95 latency than the perfect top result. Keenable built a search API around those constraints with a 100B+ page proprietary index, SQL-like query interface, and continuous benchmarking against agent-like workloads. The founders (Amazon AGI web grounding, Yandex search lead) are betting that wrapping existing search APIs won't cut it when agents become the primary consumers of web data. The architecture reveals what changes when you optimize for machine callers instead of human eyeballs. Why Agent Search Needs Different Plumbing Human search optimizes for the first three results and tolerates 500ms variance. Agent search runs in tight loops where every query blocks downstream tool calls. The contract shifts: Query volume : Agents issue 10-100x more queries per task than humans per session Latency budget : p95 matters because agents serialize tool calls; tail latency compounds across multi-step workflows Result consumption : Agents parse structured data, not blue links; relevance scoring for human click-through doesn't align with extraction success Query patterns : Agents use precise filters (date ranges, domain constraints, schema hints) that humans rarely specify Traditional search APIs built for human traffic handle agent workloads poorly. Rate limits assume sporadic queries. Pricing tiers penalize high-volume programmatic access. Relevance models optimize for engagement metrics that don't exist in agent contexts. The 100B-Page Index Decision Keenable maintains its own crawl and index instead of wrapping Google, Bing, or Brave. This is expensive but unlocks control over: Crawl strategy : Agents need fresh data on niche domains that human-centric crawlers deprioritize. A proprietary crawl can target high-churn sources (job boards, pricing pages, event listings) and re-crawl on agent-driven schedules rather than PageRank-

Image changes that would get a paper retracted are widespread among suppliers.

Get ready for a bit of a ramble about looking for a job as a software engineer in 2026. No, it's not about AI changing the definition of software engineering in 2026. But there's obviously some truth in that. It's about product engineering. Specifically, it's about the challenges engineers face when searching for new opportunities because of the massive shift toward product engineering. I should preface what comes next with this: Searching for a software engineering job in 2026 is really hard. Scroll through LinkedIn or any software career blog and you'll see plenty of posts about how the recruitment system is broken, how good engineers are being ghosted, how CVs are being filtered out by AI screening for keywords. These frustrations are valid, but... you know what else is really hard in 2026? Being a software engineering recruiter. Being a software engineering hiring manager. And software engineering is about solving problems. With that said, you can't solve a problem you don't define. So to lay the foundation, I want to address some challenges I've recognised before addressing what can be done about them. The Problem Space First, the thing that's been haunting me for the last 6 months. Impact articulation . I suspect this isn't a problem that's unique to product engineering, but it's certainly one I've faced as a product engineer. Earlier this year, I completed full interview processes with two separate companies. I felt confident about both. The roles were the type of engineering I'm great at: sitting close to users, working through ambiguity and owning product areas end to end. But neither resulted in a job offer. The feedback I received was surprisingly consistent: I demonstrated strong technical execution, methodical problem-solving, clear communication and product judgement, and consistently sought to understand the "why" behind the "how". But also, I struggled to connect my product decisions to business or user outcomes. It was clear that I was a great engin

Here's a dirty secret of search: "the closest match" and "the most useful result" are not the same thing. Return the mathematically nearest document and you'll often hand someone something technically related and practically useless. Relevance is a harder problem than similarity — and it's where good search is won or lost. Getting that right was the core challenge in the GovernAI Research Atlas , a semantic discovery platform I built to unify research across papers, repositories, and policy. Similarity is not relevance Semantic search gives you a superpower: embed everything into vectors and find items close in meaning , not just wording. But raw nearest-neighbor retrieval has a blind spot. The vector-closest result might be a tangential paper that happens to share vocabulary, while the genuinely useful one sits slightly further out. Distance in embedding space is a proxy for relevance — a good one, but not the whole story. If you stop at "closest vector," your search is clever and still frustrating. Ranking on top of retrieval The Atlas runs ChromaDB vector search with Sentence-Transformer embeddings across sources like OpenAlex and GitHub — that's the retrieval layer, the "what's semantically near this query." On top of it sits a custom relevance score that decides what actually surfaces first. That two-stage shape is the pattern behind every search system worth using: Retrieve broadly by meaning. Vectors pull in the semantically-relevant candidate set, fast, across a large and varied corpus. Rank deliberately. A custom scoring layer reorders those candidates by what's actually useful — because the job isn't to return related results, it's to return the right one first. Unify the sources. Papers, code, and governance material ranked into a single relevance-ordered experience, so discovery crosses formats instead of siloing them. Why this is the interesting part Retrieval gets the attention; ranking gets the results. Anyone can wire up a vector database and get "se

Google is giving publishers a new button that lets readers make them a preferred source across Search, Discover, and Google News, potentially boosting their traffic as AI search sends fewer clicks to the web.

ChatGPT and other AI models are now authoring and editing much of the new web.

People-search tool ClarityCheck left database containing more than 9M image files exposed.

US healthcare is broken. Under RFK Jr., the research agency working to fix it is, too.

This week shipped one of the more consequential infrastructure changes in a while: DynamoDB absorbed vector search, collapsing a common two-database architecture into one. Meanwhile, a CMU study put hard numbers on something senior engineers have suspected about AI coding tools, and a 3B parameter model posted reasoning scores that have no business coming from a model that size. DynamoDB adds native vector search without a separate database AWS added a SearchVectors API to DynamoDB, letting you store embeddings alongside your application data and query them directly—no Pinecone, no Weaviate, no synchronization layer between your transactional store and your vector index. This matters because the dual-database pattern is genuinely painful at scale. You write to DynamoDB, you write to your vector DB, you manage consistency between them, you pay for two systems, and you debug failures in both. For RAG pipelines and semantic search on data that already lives in DynamoDB, that overhead exists purely because vector search wasn't available where your data was. Now it is. Setup requires picking an embedding model (Bedrock, Cohere, or OpenAI), configuring a vector index with dimensions and distance function, and rewriting retrieval queries to SearchVectors . Vector operations are billed separately per GB across writes, reads, and storage—so run the math before assuming this is cheaper than your current setup. Verdict: Ship if you're already on DynamoDB and maintaining a separate vector DB. The architectural simplification is real. Start with a proof-of-concept on a non-critical workload to validate cost and latency before migrating production RAG infrastructure. AI coding speed spike vanishes in three months Carnegie Mellon tracked 806 repositories after Cursor adoption and found that the velocity boost disappears by month three. What doesn't disappear: a 30% increase in warnings and 41% higher code complexity that persists indefinitely and cuts future velocity by 50–64%. Th

Ruby Rose Bloom sells one-of-a-kind vintage — a self-hosted storefront, no Shopify, no marketplace underneath it. Search Console's "Merchant opportunities" report told me 3 active products weren't showing up on the Shopping tab, and I went looking for the setting to fix. There wasn't one. What I actually found, three days of digging later, is that "get into Merchant Center" is not one thing — it's several different surfaces, each fed by a different mechanism, and the one everyone talks about (the Shopping tab) turned out to be the least interesting of them. This post is the question I actually had, answered with screenshots taken today: I have a storefront. What does getting into Merchant Center buy me, and where do my products actually end up? It also has an ending I didn't plan. After three days of feed fields and structured data I opened one Search Console report I'd been ignoring and found that Google had indexed 5 of my 436 pages — and, chasing that, that essentially none of my product photos were in the image index either. Those two sections are the most useful thing here, and they're the part I'd read first if I were you. What Merchant Center actually is Before the surfaces: Merchant Center is not an ads product by default. There are two lanes. Free listings are unpaid — you register a feed, Google reviews the items, approved items become eligible to appear in Shopping-related placements at no cost per click. This is the lane a small shop should care about first, because it costs nothing beyond the engineering time to feed it correctly. Shopping ads are the paid lane on top — you attach a budget and the same feed becomes the input to a campaign. Ruby Rose Bloom is running free listings only; there is no ad spend anywhere in this post. Free listings in Merchant Center: approved items, no ad spend, click potential still "available soon" on a three-day-old account. Free listings is the whole story for this shop. Worth saying plainly since most "how to get on Goo

Search has got more powerful, but also more complicated It used to be that database queries were simple enough. Either you had an index, or you didn't, and either way you had some kind of optimizer (cost or rule) that turned your SQL statement into a viable plan for finding and returning your data. We now live in a world where in addition to the Boolean logic of traditional RDBMS queries, we also have: Geospatial queries Full text search queries, using BM25 . Vector Search And if that weren't enough, we have to consider that instead of a traditional application issuing the query, it might be an MCP server, and most importantly of all, the business need might be for two or more of these searches to happen at the same time, on the same data. For example: "An MCP server that uses a single, combined geospatial + full text query to identify towns in Bavaria with castles mentioned in text descriptions." How does CrateDB help with this? CrateDB is one of the limited number of products that not only supports all of these search types but is also capable of storing arbitrarily large quantities of data. This is important, as if you have to hit multiple different database servers to solve your business question, not only is your environment much more complicated, but you risk getting incorrect answers as your multiple databases may be out of sync. In this two-part example, based on our playable IOT Analytics scenario, we will show two things: Using Geo + Text to search a weather/tourism database Using CrateDB as a '360 view' for your MCP server If you want to follow along with this post, the setup is based on our IoT Analytics scenario. You can also just skip this blog post and jump straight to the scenario. Using Geo + Full text to search a weather/tourism database The table we're going to use is called ' German Regions ': CREATE TABLE IF NOT EXISTS demo . german_regions ( region_name TEXT PRIMARY KEY , geo_coords GEO_SHAPE , tourism_info TEXT INDEX USING FULLTEXT WITH ( anal

Short answer: for an ask-your-docs feature in a multi-tenant SaaS help center, start with embeddings over document chunks, retain keyword search for exact identifiers, and add reranking only when retrieval evaluation shows that the first-stage ordering is weak. The architecture is simple: ingest tenant-scoped chunks, embed them, store the vectors in a managed index, retrieve a small candidate set, and give only those matches to the answer model. The important marketplace constraint is less glamorous: every retrieval and model call must carry a tenant identifier into metering, or the team will know the total bill while remaining unable to explain which storefront created it. Don't begin with a vendor. Begin with the miss you can tolerate. How should a SaaS help center combine semantic search, embeddings, and keyword search? Semantic retrieval handles the normal language mismatch between a customer's question and the documentation. A user may ask how to “change the shop owner,” while the source chunk says “transfer account administration.” Keyword matching sees different tokens; embeddings map both query and chunks into vectors and can retrieve text with related meaning. That is the decisive reason to use embeddings for support questions, not fashion and not an assumption that vectors make every search problem better. Keyword search still earns a narrow, valuable lane. Error codes, plan names, API fields, invoice identifiers, and product-specific phrases often need literal matching. PAYMENT_1042 is not a semantic concept that should be softened into something approximately related. For a beginner implementation, run vector retrieval as the default and merge an exact-match result when the query contains one of those identifiers; don't build a many-stage ranking system before the corpus supplies evidence that you need one. Chunk boundaries matter because retrieval returns chunks, not abstract documents. Split by meaningful document structure, retain the page title and s

As research and AI-assisted papers surge, volunteer reviewers struggle to keep up.

The Stable Liar The dashboard was green for eight quarters The most dangerous number on a dashboard is the one that has stayed green the longest, and the way it fails has a shape you have probably watched up close. For eight straight quarters the dashboard holds green. Revenue up and to the right. Retention flat and healthy. NPS in the fifties. Every board meeting opens on the same slide and closes on the same nod. The plan is working. Then, six months after the eighth green quarter, the business the dashboard was supposed to describe nearly falls over. Pull the post-mortem apart and the easy story is that the numbers lied. They did not. Every quarter the dashboard reports something true: customers are still paying, logins are still happening, the survey scores are still fine. All of it accurate. The failure is quieter and worse than a lie. The words behind the numbers change meaning while the numbers stand still. “Retention” still counts the same logins, but a login has stopped predicting a customer who will renew. The metric keeps its shape long after the thing it measured has walked out of the room. Anyone who has run a team has felt a smaller version of this. The number you trusted most became the number that surprised you most. You were not lied to. You were tracking something that used to mean one thing and quietly came to mean another, and the dashboard had no way to tell you the meaning had moved. This is the stable liar: a number that goes on looking right long after it stopped being right. It is a structural property of measurement under pressure, and it has a law underneath it. Why every optimised metric drifts A metric is a substitution: you replace the thing you care about with something you can count, and the gap between them is where the trouble lives. Start with the substitution. You cannot measure value, loyalty, insight, or health directly, so you pick a proxy you can count. Revenue stands in for value. NPS stands in for loyalty. Citations stand in

A few notes and takeaways from the 2026 State of CSS survey results, including a nice CSS-Tricks cameo! 2026 State of CSS, Devs Surveys originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

Shopify says AI isn’t cannibalizing search traffic the way it has for publishers. Instead, AI-driven traffic and orders to Shopify stores tripled year over year in Q2.

A portfolio can look good on the usual scorecard and still answer the wrong question. One line says return was high. Another says risk-adjusted performance was acceptable. A third says drawdown stayed inside a tolerable range. Then the market turns, the benchmark starts recovering, and the thing I actually care about is different: how efficiently did the portfolio catch up? That is where a new metric can fool its own author. If I build a recovery measure and it moves almost exactly like an existing ratio, I have created a longer name for the same signal. The right acceptance test is geometric: a useful metric should cast a different shadow. This is the rule I used while validating Hyperlogarithmic Benchmark Catch-Up Ratio (HBCR): orthogonality to existing measures is a first-class test, not a chart for the appendix. 1. A new metric has to earn its axis HBCR was built to measure benchmark-relative recovery dynamics. The research page states the motivation plainly: traditional benchmark-relative metrics often fail to capture the true dynamics of investment performance, especially during market recoveries [ A New Metric for Private Equity Risk Adjusted Returns , Calibration of Risk and Correlation in Private Equity ]. That framing matters because the obvious validation path is tempting and weak. You compare the new number with familiar performance measures, find a comforting relationship, and declare victory. But a high correlation with a well-known score can be a warning. If HBCR strongly tracked Sharpe Ratio, it would probably be an expensive synonym for risk-adjusted return. The acceptance test I wanted was sharper. HBCR should have some relationship with performance, because recovery has economic content. It should also avoid collapsing into the same direction as Sharpe Ratio, Beta, Volatility, Alpha, Total Return, or Max Drawdown. Written as a predicate, the test has two sides. Let $\mathcal{T}$ be the set of metrics already on the scorecard, $\rho_{n,m}$ the corr

Bottom line: for a beginner ask-your-docs feature in a SaaS help center, I would start with embeddings-based semantic retrieval over document chunks, retain keyword search as a fallback, and add reranking only after I can measure weak top results. It is the least complicated architecture that handles the natural-language questions support teams actually receive while still giving an operator clear levers for relevance, cost, and SLOs. I learned to treat retrieval as a production dependency after a token bill landed at $8,742 for a help-center experiment I had estimated at under $1,000. The expensive part wasn't one dramatic model call; it was sending whole articles, navigation chrome, and duplicate chunks to the answering model for every vaguely phrased question. That mistake changed my order of operations: retrieve a small, attributable set first, inspect it, then generate. A chat model is a poor index. Small index. Big difference. How should a SaaS help center use semantic search, embeddings, and keyword search? Semantic search turns both a question and each document chunk into vectors, then retrieves chunks that are close in that vector space. For an ask-your-docs semantic search feature, that means a customer asking "Why can't I invite another teammate?" can reach a passage titled "Adding users to a workspace" even when the words do not line up. Keyword search remains useful for exact error identifiers, product SKUs, and freshly published terms, but by itself it is a thin answer to the way people phrase support questions. My beginner architecture is deliberately boring: export approved help-center content, strip templates and repeated navigation, split the remaining text into stable chunks, attach the page URL and heading as metadata, create embeddings, and put the vectors in a managed vector database. At question time, retrieve a modest candidate set, optionally rerank it, and pass only the best cited chunks to the chat model. Node.js can own the ingestion job