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Contextual Page Signals: Scored Entities, Brand Safety, and IAB Categories from a URL
A contextual video-advertising platform needed to know what a page is about before placing an ad on it. Point the pipeline at an article URL and it returns IAB category to tier two, entities with salience scores so the main subject reads off the structure rather than the headline, article sentiment with a confidence, a brand-safety flag with its reasons, and keywords. The brand-safety pair is the argument: two celebrity-death tributes, one flagged high_risk for death and drugs, the other safe, because the model reads the page instead of matching the word. Explore: mixpeek.com/diagrams/contextual-page-signals
21 Object Storage Providers, and the One Thing None of Them Do
We put 21 object storage providers side by side: five hyperscalers, eleven cheaper alternatives, three edge networks, one decentralized, one you run yourself. Storage price is the boring dimension, with the whole field between $6 and $25.50 per TB per month, roughly a 4x spread. Egress has no ratio at all: Google Cloud charges $120 per TB to hand your own data back, while Cloudflare R2 charges nothing, so walking out with 10 TB costs $1,200 or $0 depending only on where the bytes sit. Migration is also less painful than it looks, because 19 of the 21 expose a full or native S3 API, which makes moving mostly an endpoint change. Then there is the thing no bucket does: a list call returns keys, sizes and timestamps and says nothing about what is inside the file, so it cannot tell you which of 40,000 clips has someone holding a red umbrella. Mixpeek connects the bucket you already have, reads the contents across video, images, PDFs and audio, and makes them searchable in place. Prices re-verified against each vendor's own pricing page in July 2026. Full comparison: storage.mixpeek.com
Quantisation Scrambles What Is Near What
Generative recommendation turns items into discrete tokens so a model can generate the next item the way a language model generates the next word, and making those tokens means quantising each item embedding into a short code. In the pretrained embedding space similar items sit near each other, and that adjacency is the signal; after quantisation the intrinsic structure is disrupted, so the model's perception of item similarity is distorted before training begins. TopoTok (RecSys 2026) measures that distortion and repairs it with multi level distillation, coarse to fine: inter group for global cluster relations, intra group for local structure, inter item for fine grained alignment, reporting up to 9.42% better Recall@5. One distinction matters: this is not index-time vector compression, where you rescore the shortlist at full precision to recover accuracy. Here the structure is gone before the model trains, so there is nothing to rescore. Paper: arxiv.org/abs/2607.18600
Your Embedding Is a Point. It Should Be a Cloud.
Every retrieval system treats an embedding as a point: one vector, one location, one distance to the query. That assumption hides something, because content is not equally specific. A five second clip of someone walking is unambiguous; a two minute clip of a wedding is many things at once. Both collapse to a single point, and the system ends up exactly as confident about each. The Distribution-Alignment Bridge (Chae, Lee, Ahn, ECCV 2026) models text and video embeddings as Gaussians defined by a mean and a variance, so a vague caption becomes a wide distribution and a precise one becomes tight. Matching becomes distribution alignment rather than point distance, trained with a KL based distribution aware contrastive loss and a deterministic diffusion style bridge that refines the text distribution toward the video distribution over a truncated number of steps, evaluated on MSR-VTT, MSVD and VATEX. The payoff is calibrated uncertainty in the ranking. Paper: arxiv.org/abs/2607.20984
PLAID-PRF: Query Expansion That Reuses What Your Index Already Stores
Pseudo relevance feedback has been known since the 1970s: run the search, assume the top results are roughly right, pull useful terms out of them, and search again with a better query. It reliably improves ranking, and almost nobody runs it on modern multi-vector retrieval because the second pass costs more than the first. ColBERT keeps one vector per token and scores every query token against every document token, and PLAID makes that affordable by quantising each token vector to its nearest centroid so the index stores centroid identifiers rather than full vectors. PLAID-PRF (Wang, MacAvaney, Macdonald, SIGIR 2026) notices those centroids are already in the index and behave like tokens, so feedback can run on them directly: select a small diverse high utility set and append it to the query, for up to 4.3% better nDCG at 10 without query time clustering. Paper: arxiv.org/abs/2607.18626
Your Agent Can Read Everything. It Cannot Watch Anything.
A short vision piece on the asymmetry at the centre of enterprise data. An AI agent can read every document you own and cannot watch a single one of your videos. The meeting where the decision got made, the demo that closed the deal, the incident review, the customer interview: recorded, stored, and effectively not retrievable by what is inside them. Text got a fifty year head start, since the relational model dates to 1970 and full text retrieval is about as old, so querying structured text is a solved problem with mature tooling at every layer. Everything else piled up unindexed. Mixpeek indexes video, images, audio and documents together, decomposing objects into features so a retriever can reach inside them, which is how agents get eyes and ears through one API. Explore: mixpeek.com/glossary/multimodal-data-warehouse
The Retrieval Funnel: Why Your Reranker Sees 400 of 10 Million
Ten million documents could answer one query, and the model best qualified to judge them, a cross encoder that reads query and document together, is the one you can least afford to run across a corpus. That single constraint is why production retrieval is a ladder rather than a model. ANN search over embeddings goes first because it is cheap per document, so it runs against everything and returns a shortlist. Filters cut further, and the order matters: filtering after scoring and filtering before scoring are different operations with different recall. Only then does the cross encoder run, affordable precisely because it inherits a shortlist. Cheap stages absorb the volume, costly stages inherit a shortlist. Full breakdown: mixpeek.com/glossary/multi-stage-retrieval-pipeline
HNSW: How Vector Search Skips 10 Million Comparisons
Ten million vectors sit in an index and a query arrives. Comparing against every one is exact and unusable, so vector databases run HNSW instead. This video is a live simulation of the algorithm: build a proximity graph, walk it greedily toward the query, then watch why a single flat graph makes that walk cross the entire space. HNSW stacks the graph into layers, so a thin top layer with long links covers enormous ground in a couple of hops and the best node found becomes the entry point one level down. Coarse to fine, logarithmic rather than linear. It is approximate, and widening the candidate list raises recall and latency together, which is the real operating decision. Full breakdown: mixpeek.com/guides/approximate-nearest-neighbor-algorithms
FLUX 3: Generation Unified. Storage Didn't.
Black Forest Labs' FLUX 3 is one network that jointly learns images, video, and audio: 20 second clips with sound in a single pass, preference wins over Runway Gen-4.5 (77%) and Luma Ray 3.2 (93%), and the same backbone powers FLUX-mimic, a video-action model for robots built with mimic robotics. This video walks the announcement, then the downstream gap: generation collapsed into one architecture while most storage stacks still split video, audio, and images into three systems that cannot search each other. Mixpeek is the warehouse side of that pipe: every modality indexed together, so generated media stays findable. Announcement: bfl.ai/blog/flux-3