गर तू है लक्खी बंजारा
और खेप भी तेरी भारी है
ऐ ग़ाफ़िल तुझ से भी चढ़ता
इक और बड़ा ब्योपारी है
सब ठाठ पड़ा रह जावेगा
जब लाद चलेगा बंजारा।
तू बधिया लादे बैल भरे
या सूद बढ़ा कर लावेगा
धन दौलत नाती पोता क्या
इक कुनबा काम न आवेगा
सब ठाठ पड़ा रह जायेगा
जब लाद चलेगा बंजारा।
नज़ीर अकबराबादी
Just watched a video where a woman writer shared how her mum came over to help. Her mum did all the housework, laundry, cooking, cleaning, and childcare so she could focus. During her mum’s stay, writer TRIPLED her daily word count.
Then she said something that hit hard.
Most men have this kind of support their entire lives. Next time you call a man productive, consider that.
Damn. Still letting it sink in.
Computer science principles (cheat code)
Here you go:
- Hashing to get quick lookups.
- Sorting to get quick searches.
- Append only to get fast and high throughput writes.
- In-memory to get ultra fast writes/reads.
- Probabilistic data structures to get fast lookups with chances of false positives.
- B-trees to get quick lookups with disk-friendly access patterns.
- Bloom filters to get space-efficient membership testing with acceptable false positives.
- Write-ahead logging to get durability without sacrificing write performance.
- Caching to get fast reads by storing frequently accessed data in faster storage.
- Indexing to get quick searches without scanning entire datasets.
- Compression to get reduced storage costs at the expense of CPU overhead.
- Sharding to get horizontal scalability by distributing data across multiple nodes.
- Replication to get high availability and read performance through data redundancy.
- Columnar storage to get fast analytical queries by storing related data together.
- LSM trees to get high write throughput by batching writes and periodic merging.
- Skip lists to get balanced tree performance with simpler lock-free implementations.
- Consistent hashing to get even data distribution with minimal reshuffling during scaling.
- Trie structures to get fast prefix matching and autocomplete functionality.
- Ring buffers to get bounded memory usage with efficient circular data access.
- Copy-on-write to get memory efficiency by sharing data until modifications occur.
- Merkle trees to get tamper detection and efficient synchronization through cryptographic hashing.
- Segment trees to get fast range queries with logarithmic update complexity.
- Fenwick trees to get efficient prefix sum calculations with minimal memory overhead.
- Union-find to get fast connectivity queries through path compression and union by rank.
- Suffix arrays to get efficient string matching with reduced memory compared to suffix trees.
- Inverted indexes to get fast full-text search by mapping terms to document locations.
- Spatial indexing to get quick geographic queries through multi-dimensional partitioning.
- Time-series databases to get optimized storage for chronological data with compression.
- Event sourcing to get complete audit trails by storing state changes instead of current state.
- CRDT (Conflict-free Replicated Data Types) to get eventual consistency without coordination overhead.
- Lockless data structures to get high concurrency through atomic operations and memory ordering.
- Partitioning to get improved performance by dividing data based on access patterns.
- Materialized views to get fast complex queries by pre-computing and storing results.
- Delta compression to get reduced storage by storing only differences between versions.
- Heap data structures to get efficient priority queue operations with constant-time peek.
- Rope data structures to get efficient string concatenation and manipulation for large texts.
- Radix trees to get memory-efficient prefix storage through path compression.
- Adaptive data structures to get self-optimizing performance based on access patterns.
- Batching to get improved throughput by amortizing overhead across multiple operations.
it’s so amazing when you’re handwriting and you get to a word with two t’s and you get to do one big line across for both of them. life can be amazing occasionally though also still very painful
Serena Williams introduces Maria Sharapova to the Tennis Hall of Fame
"I know I'm probably the last person you'd be expecting to see tonight”
"We had our differences. To the world, we looked miles apart, but the truth is we weren't”
All of this. 🥹