标签：#search

Now, when users navigate to Google Images, they'll see a "For You" gallery of images tailored to their interests and browsing history.

We'll get back to you. It's a sentence almost every job seeker has heard. For some, those words become the beginning of a new career. For many others, they become another unanswered promise. But the truth is, an interview doesn't begin when someone asks, Tell me about yourself . For millions of job seekers, it begins much earlier. Before the Interview Even Begins It's 6:45 in the morning. The alarm rings. A young professional stands in front of the mirror, adjusting the outfit they've carefully prepared the night before. He checks his resume one last time, gathers his documents, confirms the location, and takes a deep breath. As he’s about to leave, someone at home asks, “Do you think this one will work out?” He smiles. “I hope so.” He walks out carrying more than a folder. He carries expectations, financial pressure, family responsibilities, and the quiet hope that this interview might finally change everything. The Hidden Cost Nobody Talks About People talk about skills, preparation, and confidence. Those matter. But there’s another side rarely discussed: the hidden costs. Transportation. Professional clothing. Internet bills. Certification courses. Resume updates. Travel. Meals. Even taking a day off from a part-time job or missing freelance work. For someone without steady income, these aren’t just expenses — they’re investments with no guaranteed return. Sometimes they lead to an offer. Often, they end in rejection or silence. A Resume Can Tell You Skills. It Can’t Tell You a Story. A resume tells recruiters what a candidate has done. It doesn't tell them what they're carrying. It doesn't reveal the father waiting for good news, the mother asking how it went, the EMI due next week, the rent that can't wait, or the confidence slowly wearing down after repeated rejections. When Expectations Change Candidates prepare for the role they applied for. Sometimes they discover the responsibilities, salary, or even the position itself has changed. Business priorities evo

Searching billions of documents for a phrase and getting ranked results in tens of milliseconds looks like magic. It is not. It comes down to two ideas working together: an index that maps words to documents instead of scanning documents for words, and a way to spread that index across machines so each holds only a slice. Understand both and full-text search stops being mysterious. The core problem A database scans rows. If you ask a plain database to find every document containing a word, it reads documents and checks them, which is linear in the amount of data. That is fine for exact key lookups and hopeless for free-text search across huge corpora. You need the opposite mapping. Instead of "given a document, what words does it have", you want "given a word, which documents have it". That inversion is the whole trick. The second problem is size. One machine cannot hold the index for billions of documents, and one machine cannot serve the query load. So the index has to be split across nodes, and a query has to find the right nodes and combine their answers. Key design decisions Build an inverted index. At index time, each document is broken into tokens by an analyzer that lowercases, splits on word boundaries, and often strips or stems words. For every token, the engine keeps a posting list: the set of document ids that contain it, often with positions for phrase matching. A query for a word becomes a direct lookup of its posting list, not a scan. A multi-word query intersects or unions posting lists, which is fast because the lists are sorted. Store the index in immutable segments. New documents go into small new segments rather than editing existing ones. Segments are immutable, which makes them cache-friendly and safe to read without locks. A background process merges small segments into larger ones over time. A delete is just a marker; the document is removed for real during a later merge. Split an index into shards. An index is divided into shards, each a sel

Every system that does "semantic" anything — RAG pipelines, recommendation engines, image search, dedup — boils down to one operation: given this vector, find the closest ones out of millions. The vectors are embeddings, a few hundred to a couple thousand numbers each, and "closest" means closest in meaning. You'd assume the database either scans all of them (slow but correct) or uses some clever tree to jump straight to the answer. It does neither. Instead it deliberately settles for the approximately closest vectors — and that compromise is the entire reason vector search is fast enough to exist. Two algorithms do almost all the heavy lifting in practice, in pgvector, Qdrant, FAISS, and the rest: IVF and HNSW . Here's what they're actually doing under the hood, and how to choose between them. Why "exact" is off the table The natural objection is: why approximate? Just find the real nearest neighbor. In two or three dimensions you could — a k-d tree or similar structure prunes away big regions of space and finds the true closest point quickly. The trouble is that embeddings live in hundreds of dimensions, and high-dimensional space is deeply weird. It's called the curse of dimensionality . As dimensions grow, the distance to your nearest point and the distance to your farthest point drift toward being almost the same. Formally, the contrast (d_max − d_min) / d_min shrinks toward zero. When everything is roughly equidistant from everything else, a tree can't confidently say "skip this whole branch, it's too far" — the bounding regions all overlap, every branch looks plausible, and the search degrades into checking nearly everything. Exact indexes quietly collapse back into brute force. So we change the question. Instead of "prove you found the nearest," we ask "quickly find something very probably among the nearest." That's approximate nearest neighbor (ANN) search, and it swaps a guarantee for speed. The quality knob becomes recall : of the true top-k neighbors, wh

A new analysis from OpenAI reveals issues in SWE-Bench Pro, a popular coding benchmark, raising concerns about reliability and accuracy in evaluating AI models.

Verity Harding tells WIRED that the US government’s nationalistic attitude toward AI is evidence that a worst-case scenario is taking shape.

PSA: A change to Google's privacy settings let it train its AI on more of your data. Here's how to opt out.

EDRSR — the Unified State Register of Court Decisions — is effectively all of Ukraine's judicial practice in open access. Today Qdrant holds **44M+ vectors : criminal (19M), civil (14.3M), commercial (5.1M), misdemeanors (5.6M). Vectorization of civil cases (CPC, justice_kind=1) — the largest cohort at 33.7M documents — runs on a dedicated EC2 instance (r6a.xlarge, 32 GB RAM, 2 TB gp3). Here's what's under the hood: models, pipeline, cost, rakes, and current status. Why Vectorize Courts When a lawyer searches "is there case law on recovering bank prepayment fees" — they don't want to open 40 decisions and read them through. They want the system to surface the top 5 most relevant ones, pull out key paragraphs, and show how courts reasoned. Full-text search (FTS) over keywords doesn't give that — it returns every document containing the word "fee", and there are thousands. For this semantic task you need vector representations of text. The model turns a paragraph from a decision into a point in a 1024-dimensional space; semantically similar paragraphs sit near each other. A kNN search in Qdrant returns the top K nearest, and an LLM composes the answer from exactly those relevant fragments. The only problem: the register is big. Very big. Scale Our prod database holds full texts of decisions starting from 2006. Breakdown by procedural type: Civil (CPC) — 33.7M documents. The largest category. Consumer, housing, labor, family. Criminal (CrPC) — 12M+ Administrative (CAS) — 14M+ Commercial (CC) — 6M+ Misdemeanors (CUaP) — 6M+ The Qdrant collection edrsr_decisions on a dedicated EC2 currently holds 44M+ vectors (122 segments, on_disk=true): | Proceeding type | justice_kind | Vectors | |—|—|—| | Criminal (CrPC) | 2 | 19,036,347 | | Civil (CPC) | 1 | 14,328,427 | | Misdemeanors (CUaP) | 5 | 5,579,432 | | Commercial (CC) | 3 | 5,098,662 | | Total | | 44,042,868 | Civil cases processed: 14.3M out of 33.7M — that's 42%. After CPC completes there will be roughly 63M+ vectors in

Also, the science of poop's distinctive shape, boron buckyballs, and the secret to a soccer feint.

Elastic open-sourced Atlas, a system built on Elasticsearch that maintains three categories of memory for agents. Atlas integrates with agents via MCP and maintains per-user isolation of memories. When evaluated on question-answering capability, it scored 0.89 Recall@10. By Anthony Alford

Introducing GeneBench-Pro, a new benchmark testing AI performance in genomics, biology, and scientific research using complex, real-world datasets.

Why Your Agent's Search Results Look Right and Are Wrong: The Index Distribution Problem You've built an agent. It has a search tool. You query it with something reasonable — a factual question, a comparison, a technical lookup — and it returns results. The results look right. The sources are real. The snippets are plausible. The agent synthesizes them into a confident answer. And the answer is wrong. Not obviously wrong. Not hallucinated-in-a-hallucinatory-way wrong. Structurally wrong — wrong in a way that passes every surface-level check because the error is baked into the retrieval layer before the model ever sees the context. This isn't a prompt engineering problem. It isn't a context window problem. It's a distribution problem , and it has a structural ceiling that no amount of better prompting will fix. The Index Is a Frozen Decision Here's the thing most agent builders don't internalize: a search index is not a neutral representation of knowledge. It's a frozen set of decisions about what matters and what doesn't. Every index — whether it's a BM25 inverted index, a dense vector store, or a commercial web search API — encodes a distribution shaped by past relevance judgments. Someone, at some point, decided which documents were "relevant" to which queries. That could be explicit (human raters labeling search results) or implicit (click logs, dwell time, link graphs). Either way, the index now encodes a probability distribution over what the system considers a good answer to a given query. That distribution is not semantic truth. It's past relevance consensus . Consider what happens when you embed a corpus and build a vector index. Your embedding model was trained on data that reflects certain assumptions about what concepts are close to each other. Your chunking strategy encodes assumptions about what granularity of information is useful. Your ranking model — whether it's cross-encoder reranking or a learned relevance model — was trained on labeled data that

Researchers have demonstrated they can make coffee comparable to conventional espresso using ultrasonic waves. Because the process doesn’t need hot water, it consumes 75 percent less energy.

You type "k8s deployment troubleshooting" into your documentation search. The top result is a page about Kubernetes architecture that never mentions the word "troubleshooting." It is exactly what you need. BM25 would have missed it entirely. This is the promise of vector search: finding documents by meaning, not just matching words. In 2025 and 2026, vector search has moved from niche ML engineering to a core Elasticsearch capability. If you are building search for AI applications - RAG pipelines, semantic Q&A, recommendation systems - understanding how Elasticsearch handles vectors is no longer optional. I have spent the past year building RAG pipelines at Cloudera, and I have learned that vector search is powerful but easy to misuse. This post covers what works, what does not, and how to implement it in production. Why Vector Search Matters (And When It Does Not) BM25, which we covered in a previous post, is brilliant at matching exact terms. But it is fundamentally lexical. It does not understand that: "k8s" and "kubernetes" are the same thing "docker container" and "containerization" are related concepts "out of memory error" and "heap exhaustion" describe the same problem Vector search solves this by converting text into high-dimensional numerical vectors (embeddings) where semantically similar content lives close together in vector space. A query for "k8s deployment troubleshooting" gets embedded into a vector, and Elasticsearch finds the nearest document vectors - even if they do not share a single keyword. But vector search is not a replacement for BM25. It is a complement. BM25 is faster, requires no ML infrastructure, and excels at exact-term matching. Vector search is slower, requires embedding models, and shines at conceptual similarity. The best search systems in 2026 use both. How Elasticsearch Stores and Indexes Vectors Elasticsearch introduced the dense_vector field type in version 7.x and has dramatically improved it through 8.x and into 2026. Here

OpenAI and Molecule.one show how a near-autonomous AI chemist using GPT-5.4 improved a key drug-making reaction, advancing medicinal chemistry research.

Introducing LifeSciBench, an expert-authored, expert-reviewed benchmark for evaluating how AI systems handle real-world life science research tasks and decisions.

WordPress VIP’s latest survey suggests consumers are wary of AI-generated answers even as companies increasingly view AI search as an important referral channel.

OpenAI introduces Deployment Simulation, a method to predict AI model behavior before deployment using real conversation data to improve safety and evaluation accuracy.

Google AI Overview court loss in Germany could spell doom for AI search industry.

Amazon Web Services has recently announced the general availability of the next generation of Amazon OpenSearch Serverless, with a redesigned architecture that enables 20 times faster resource provisioning than the previous serverless architecture, true scale-to-zero capability, and up to 60% lower cost than a provisioned cluster for peak loads. By Gianmarco Nalin