Backend engineer thinking about architectures for AI-driven systems. Building at marcopolo.dev

Lima, Peru
Based in Peru
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Frontier AI programming needs a sub, hand code programming used to be free
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I’m building a spanish quotr evaluation :)
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Replying to @maria_rcks
please add zcode as t3code provider or at least add a plugin architecture way of add my weird subs (like factory droid lol)
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Replying to @midudev
estoy de acuerdo con que frenen un poco el desarollo si esto implica utilizar los esfuerzos en seguridad y mejorar el performance, claude tiene modelos inteligentes pero carísimos y parece no importarle por el solo hecho de ganar la carrera, por cierto qué tal el ceviche?
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Replying to @tavo_serpa
mental quizás, le quedó grande la camiseta
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un agente puede hacer un workflow de n8n a ojos cerrados
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a mi rabanal me parece un jalesazo
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Replying to @SitoCinema
4 niños 4 piernas, 12 niños, 12 piernas xd
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esa plata la meten al empate XD y ganan mas
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interesring
🚨 Google published a 69-page prompt engineering masterclass. This is what's inside:
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Replying to @fanovargas
Pense que era el unico webon esperando su codigo
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Replying to @fanovargas
se ve 3 veces más grande que el matute
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Replying to @InformaCosmos
Perucito
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Replying to @natayadev
Trabajo con varios data en finder, soy más de software pero sé algunas cosas xd c puede?
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Replying to @jigglypurintail
La biblioteca de Merlin 🫶🏽
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Replying to @maps_black
hay pumas en argentina?
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🧐
PGVECTOR IS NOW FASTER THAN PINECONE. And 75% cheaper thanks to a new open-source extension – introducing pgvectorscale. 🐘 What is pgvectorscale? Pgvectorscale is an open-source PostgreSQL extension that builds on pgvector, enabling greater performance and scalability (keep reading for the actual numbers). By using pgvector and pgvectorscale, developers can build more scalable AI applications, benefiting from higher-performance embedding search and cost-efficient storage. 📈 How does it perform? On our benchmark of 50 million Cohere embeddings (768 dimensions each), PostgreSQL with pgvector and pgvectorscale achieves 28x lower p95 latency and 16x higher query throughput compared to Pinecone for approximate nearest neighbor queries at 99 % recall, all at 75 % less cost when self-hosted on AWS EC2. We also tested it against Pinecone’s p2 high performance index, see the blog post at the end of this post for full results (spoiler: It’s just as impressive). 🤔 Why did we build pgvectorscale? Our team at @timescaledb built pgvectorscale to make PostgreSQL a better database for AI and to challenge the notion that PostgreSQL and pgvector are not performant for vector workloads. ⚙️How does it achieve such good performance? Pgvectorscale brings specialized data-structures and algorithms for large-scale vector search and storage to PostgreSQL as an extension, including: (1) StreamingDiskANN –  a high-performance, cost-efficient vector search index for pgvector data inspired by research at Microsoft, and (2) Statistical Binary Quantization (SBQ), developed by Timescale’s own researchers to improve upon standard binary quantization techniques. These innovations help PostgreSQL deliver comparable and often superior performance than specialized vector databases like Pinecone. 👏 Big shoutout to @cevianNY and @sql_johnpruitt, two senior staff engineers at Timescale, who worked on these technical breakthroughs. 🧑‍💻 Sounds exciting! How can I get started? Pgvectorscale is open-source under the PostgreSQL license, and free to use on any PostgreSQL database. You can find installation instructions on the pgvectorscale GitHub repository (see end of post).  It’s also available on any database service in Timescale’s PostgreSQL cloud platform. 📚Learn more [1] Pgvectorscale explainer blog: tsdb.co/avtharvectorscale [2] Pgvectorscale github repo: github.com/timescale/pgvecto… Share this post with your followers to let them know about pgvectorscale and comment your reactions and questions.
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