Bloomfilter If algo says not found then takes the entered name If it says not found then proceed for checking to final source of truth (indexed db)

icymi: the ampersend skill got a major upgrade last week. the skill is the instruction set that teaches an ai agent how to use ampersend. every interaction with an ampersend-enabled agent runs through it, which means changes to the skill are changes to what your agent can actually do. the new version ships with 14 capability categories, each backed by a validated provider. web search via Firecrawl, email via AgentMail, voice calls via StablePhone, image and video via StableStudio, domain registration via Bloomfilter, plus job search, travel, real-world purchases, social data, news, property valuation, file hosting, and llm inference. every entry maps to a real x402 endpoint with a ready-to-run command. the agent can also search the live marketplace from inside the conversation, filtering by source, category, and network, with pricing in human-readable $usdc. onboarding scales with how experienced the user is. first-time users get curated suggestions on first run. intermediate users browse the marketplace inside their agent. power users run fetch against any x402 endpoint directly. try the new skill with @openclaw or any other agent: ampersend.ai/skill.md

today, we shipped a major upgrade to the ampersend skill, the instruction set that teaches an ai agent how to use ampersend. the skill is what turns a fresh agent into one that can actually transact. it tells the agent what ampersend does, how to call paid endpoints, and how to walk a user through a request. every interaction a user has with an ampersend-enabled agent runs through this file, so improving the skill directly improves the product. here's what we shipped today: 1️⃣ 14 capability categories with validated providers, not ai-generated guesses. web search via Firecrawl, email via AgentMail, voice calls via StablePhone, image and video generation via StableStudio, domain registration via Bloomfilter, job search via StableJobs, travel search via StableTravel, real-world purchases via Laso, plus social data, news, property valuation, file hosting, and llm inference. every entry maps to a real endpoint with a ready-to-run command. 2️⃣ live marketplace search with filtering by source, category, and network. the agent can now browse the full ampersend marketplace from inside the conversation. pricing surfaces in human-readable usdc and results compose with existing fetch workflows. 3️⃣ zero-to-discovery in one command. no setup, no api keys, no context switching. the agent suggests, surfaces, and executes paid services in the same conversation. 4️⃣ three-tier onboarding that scales from first-time users to power users. new users get curated suggestions on first run. intermediate users browse the marketplace from inside their agent. power users run fetch against any x402 endpoint directly. an agent running the old skill waited for instructions. an agent running today's skill opens with concrete things to try and runs them on the spot. try ampersend skill with your @openclaw or other agent: ampersend.ai/skill.md

made bloom-bloom v0.1 a bloom filter written in Rust [link in replies] it implements - a mutable single threaded BloomFilter - an atomic AtomicBloomFilter for concurrent inserts and reads - configurable bit count and hash count - double hashing for efficient multi probe lookup - fast modulo free index mapping, etc this is just the basic version for now, will work on this more The benchmark for 100k items are pretty good, with present lookup going for ~4.6 nanoseconds per item missing ookup going for ~3.9 nanoseconds per item will optimize it more

from pybloom_live import BloomFilter But, Redis Bloom > local Bloom Filters

Ever wondered how Instagram checks usernames so fast?😊 Bloom Filters! This clever data structure answers one ques: "Have I seen this before?" in microseconds, using just a few MBs of RAM. No heavy database lookups, just instant yes or no. #SystemDesign #Instagram #BloomFilter

Bloomfilter

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False positives ≠ inconvenience False positives = trust erosion #Bloomfilter

home-mixer::util (including get_related_post_ids and BloomFilter) is marked “excluded from open source release,” so I can’t see exact logic of that to properly fix the problem. Please consider punching whoever did that in the balls.

Launched Apache DataSketches Rust Initiative! github.com/apache/datasketch… Background discussion: github.com/apache/datasketch… Current repository: github.com/apache/datasketch… Current implemented sketches: CountMin Frequencies HyperLogLog TDigest Other under construction: BloomFilter Theta (partially) ... any sketch available in Apache DataSketches Java/C /Go version Welcome to take a look and join the porting and implementing party :D

The Bloom Filter: Probabilistic Speed for Massive Scale ⚡ In HLD, how do you check if a row exists in a 10TB database without doing a disk I/O? Enter the Bloom Filter. It’s a space-efficient probabilistic data structure that tells you one of two things: "The data definitely does not exist." (100% accurate) "The data might exist." (Small chance of false positive) The HLD Win: Systems like Cassandra or Google BigTable use this to skip checking partition files on disk. If the Bloom Filter says "No," you save an expensive I/O operation. The Trade-off: You trade a tiny bit of accuracy (false positives) for massive speed and memory savings. #SystemDesign #BloomFilter #Database #Scalability #Algorithms #SoftwareEngineering

Ever tried to size a bloom filter for petabyte-scale workloads? It's a nightmare 😅 Standard bloom filters need upfront capacity planning. Undersize = degraded accuracy. Oversize = wasted memory. Apache Hudi's dynamic bloom filter solves this elegantly: 📈 Growth Phase New filters chain dynamically when capacity exceeded — no re-hashing needed 🔄 Bounded Phase At max capacity, round-robin distribution across filters for graceful degradation The design philosophy: "Soft failure over hard failure" ➡️ Extra I/O from false positives = annoying but recoverable ➡️ Executor crashes from unbounded memory = catastrophic Result: Petabyte-scale upserts without manual tuning or configuration overhead 🎯 @yongkyun_lee's deep dive into the implementation 👇 codepointer.substack.com/p/a… #ApacheHudi #DataEngineering #DataLakehouse #BloomFilter #BigData

Se você puder ter gaps no seu número de contas usar um serial do Postgres garante isso bem de boa. Particularmente não vejo motivos para não poder ter gaps. Outra estratégia seria pré alocar as contas e você busca uma livre. Pra acelerar o lookout você pode usar um bloomfilter

HyperLogLog と BloomFilter で何か面白いものが作れそうだなー、と思うなどした

⚔️ Meet the new #Lancelot! Haocheng Liu. has been contributing to #Lance 2.1 formats #ZoneMap & #Bloomfilter indices since 2025 Spring. Coming from #fintech, he is fascinated by the new hashtag#multimodal data warehouse domain.

Yes but the people who are assholes on here are also assholes IRL X is a Bloomfilter for IRL assholes

12/n So, the next time you need to prevent username reuse, check if a user has already seen a post, or block known malicious IPs, consider using a Redis Bloom filter. It's a simple, scalable, and incredibly performant solution. ⚡️ #Redis #BloomFilter #Database #WebDev #SystemDesign

The number of repositories under an individual user is presumably a relatively small set. Github could have the whole list pre-loaded on the page. Or if you were super concerned about size, use a bloomfilter and only send a request on collisions.