Syed Anas Mohiuddin found the same SSRF flaw in MCP servers from five unrelated organisations, including Google, JPMorgan and two governments. Each server turned an address supplied through an agent into an outbound request without checking where it resolved, and any team running its own tool servers can check for the same gap.
Reality
- Evidence64
- Adoption48
- Hype gap+22
- Incentives40
- Confidence62
buildOne report1 publisher A dedicated vector database adds a sync job to every write
A dev.to post argues that moving embeddings out of Postgres buys a second source of truth, a four-leg query path and 50 to 150 ms of internet latency. Its 8 ms pgvector counter-figure has no benchmark behind it.
Reality
- Evidence24
- Adoption
- Not scored
- Hype gap+55
- Incentives55
- Confidence45
buildOne report1 publisher ACORN moves the filter into the HNSW graph walk with predicate subgraph traversal
The paper reports 2 to 1,000 times the throughput of prior filtered-search methods at fixed recall, and says an existing HNSW library is enough to implement it. Both claims turn on how the graph is built.
Reality
- Evidence46
- Adoption
- Not scored
- Hype gap+35
- Incentives65
- Confidence50
buildOne report1 publisher AiSAQ holds query-time RAM at 10 MB on a billion-vector index by moving PQ codes to SSD
DiskANN keeps product-quantized vectors for every node resident in RAM, so its footprint tracks the corpus. A paper on arxiv puts those codes on storage instead and reports millisecond-order latency at 95 percent 1-recall@1.
Reality
- Evidence40
- Adoption15
- Hype gap+20
- Incentives55
- Confidence45
buildOne report1 publisher A Postgres default of 40 caps how many neighbors your vector search can return
pgvector ships hnsw.ef_search at 40, the size of the candidate list its graph walk keeps in flight, and a top-20 query with a tenant filter can come back with two rows and no error to explain it.
Reality
- Evidence62
- Adoption
- Not scored
- Hype gap+12
- Incentives20
- Confidence58
buildOne report1 publisher LatticeDB's traversal speed margin over SQLite narrows by three hops, then widens sharply by depth ten
An independent rebuild of the SQLite side says the gap is real, but the published tables measure two different queries on two different graphs, so the depth at which SQLite's plan collapses on your data is a number you have to take yourself.
Reality
- Evidence55
- Adoption10
- Hype gap+30
- Incentives60
- Confidence50
buildOne report1 publisher Disproving one pointer in Lemmalog retracts every conclusion that rested on it
Jordy Zomer built a Datalog engine so an agent maintains what it currently knows instead of searching its own transcript, and his own benchmark runs put the weak link in the model that writes the facts.
Reality
- Evidence46
- Adoption12
- Hype gap−8
- Incentives28
- Confidence54
buildOne report1 publisher Agent memory products differ on one thing: whether anything decides a fact is dead
A comparison of Mem0, Zep and LangChain's memory classes puts the split at adjudication rather than storage. Mem0 pays two model calls per message to get it.
Reality
- Evidence42
- Adoption
- Not scored
- Hype gap+12
- Incentives
- Not scored
- Confidence38
buildOne report1 publisher The query a vector index cannot answer, whatever you embed it with
A consultant's two client failures, a supplier filter and a 10kg shipping quote, both broke on a numeric comparison that top-k similarity does not perform.
Reality
- Evidence32
- Adoption24
- Hype gap+14
- Incentives42
- Confidence46
buildOne report1 publisher Your RAG Cannot Find SKU-4471, And A Bigger Embedding Model Will Not Help
Vector search ranks by meaning, so literal tokens like part numbers and ticket IDs fall just outside the top results. The fix is lexical plus dense retrieval, not a model upgrade.
Reality
- Evidence44
- Adoption58
- Hype gap+8
- Incentives38
- Confidence52