--
[0] Fast.ai: Can Models learn from one sample, https://www.fast.ai/posts/2023-09-04-learning-jumps/ / https://archive.is/eJMPR
[1] LlamaIndex: Advanced RAG, https://blog.llamaindex.ai/a-cheat-sheet-and-some-recipes-fo... / https://archive.is/qtBXX
[2] Microsoft: RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study, https://arxiv.org/html/2401.08406v2#S6 / https://archive.is/UQ8Sa#S6"},"story_title":{"matchLevel":"none","matchedWords":[],"value":"LoRA from scratch: implementation for LLM finetuning"},"story_url":{"matchLevel":"none","matchedWords":[],"value":"https://lightning.ai/lightning-ai/studios/code-lora-from-scratch?view=public§ion=all"}},"_tags":["comment","author_ignoramous","story_39091777"],"author":"ignoramous","comment_text":"From what I gather, fine-tuning is unreasonably effective [0] because in-context learning really depends on how powerful the underlying model is and just how you do RAG (process queries, retrieve embeddings, rank outcomes, etc [1]). Per this paper I read, fine-tuning may add new domain knowledge (but as another commenter pointed out, knowledge is better represented from data of the pre-training stage) or boost specific knowledge; while RAG is limited to boosting only; nevertheless, both techniques turn out to be similarly capable with different trade-offs [2].
--
[0] Fast.ai: Can Models learn from one sample, https://www.fast.ai/posts/2023-09-04-learning-jumps/ / https://archive.is/eJMPR
[1] LlamaIndex: Advanced RAG, https://blog.llamaindex.ai/a-cheat-sheet-and-some-recipes-fo... / https://archive.is/qtBXX
[2] Microsoft: RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study, https://arxiv.org/html/2401.08406v2#S6 / https://archive.is/UQ8Sa#S6","created_at":"2024-01-22T19:54:47Z","created_at_i":1705953287,"objectID":"39094514","parent_id":39093873,"story_id":39091777,"story_title":"LoRA from scratch: implementation for LLM finetuning","story_url":"https://lightning.ai/lightning-ai/studios/code-lora-from-scratch?view=public§ion=all","updated_at":"2024-09-20T16:18:43Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"Desitrain22"},"comment_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["llamaindex","rag","2026"],"value":"Location: New York (NYC), NY (In person / Hybrid strongly preferred).
Remote: Open to it.
Willing to relocate: willing to travel frequently, but not relocate from NYC
Technologies: RAG, AI, Python, Llamaindex, Elasticsearch, pretty much everything related to ETL (Kafka, DynamoDB/MongoDB, S3, CI/CD, Kubernetes, Airflow)
NYC based backend / data / infra / solutions/ forward deployed engineer with 4 years of experience. Owned infra, product, and sales cycles from tip to tail. Currently built an enterprise RAG from the ground up and looking for a preferably NYC based start up. Also am a stand up comedian, come check out my tech themed comedy shows @NotSoDailyStandUp
resume: https://neal-patel-resume-08-01-2025.tiiny.site/
email: NealPareshPatel [ a t ] g'ma'il. c o m (you know the drill)"},"story_title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["2026"],"value":"Ask HN: Who wants to be hired? (August 2025)"}},"_tags":["comment","author_Desitrain22","story_44757792"],"author":"Desitrain22","children":[44935030],"comment_text":"Location: New York (NYC), NY (In person / Hybrid strongly preferred).
Remote: Open to it.
Willing to relocate: willing to travel frequently, but not relocate from NYC
Technologies: RAG, AI, Python, Llamaindex, Elasticsearch, pretty much everything related to ETL (Kafka, DynamoDB/MongoDB, S3, CI/CD, Kubernetes, Airflow)
NYC based backend / data / infra / solutions/ forward deployed engineer with 4 years of experience. Owned infra, product, and sales cycles from tip to tail. Currently built an enterprise RAG from the ground up and looking for a preferably NYC based start up. Also am a stand up comedian, come check out my tech themed comedy shows @NotSoDailyStandUp
resume: https://neal-patel-resume-08-01-2025.tiiny.site/
email: NealPareshPatel [ a t ] g'ma'il. c o m (you know the drill)","created_at":"2025-08-08T01:40:31Z","created_at_i":1754617231,"objectID":"44832498","parent_id":44757792,"story_id":44757792,"story_title":"Ask HN: Who wants to be hired? (August 2025)","updated_at":"2026-03-05T22:30:20Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"enouri"},"comment_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["llamaindex","rag","2026"],"value":"Location: Paris, France
Remote: Yes (for flexibility, not a requirement)
Willing to relocate: Yes
I\u2019m Nouri, a senior engineer and architect with 15+ years of experience. I\u2019ve worked for major companies like Intel, Airbus, Canon, CEA (French Atomic Agency), and SNCF, and I\u2019ve also founded and built 11 startups, mostly bootstrapped and focused on SaaS, mobile B2B, and B2C.
I\u2019m now wrapping up my latest startup (no PMF \u2014 product okay but no revenue model): Soir\u00e9es, an AI photo-sharing platform used in 13 countries by 120K+ users, powering events like Paris Fashion Weeks, Euro 2024, and the Paris Olympics 2024. I handled everything from architecture and product to growth, GTM, and AI-powered privacy.
What I\u2019m looking for: a stable, long-term role on a serious product, ideally where I can contribute across architecture, AI, product thinking, and execution. I thrive in roles that blend code, systems, and business alignment.
Technologies
Cloud: AWS (Lambda, API Gateway, DynamoDB, Fargate, SageMaker), GCP, Aliyun, OpenStack
Infra & DevOps: Kubernetes, Docker, Terraform, Serverless Framework, CI/CD
Backend: Python, Node.js, TypeScript, GraphQL, REST, gRPC
Frontend & Mobile: React, React Native, Swift, Kotlin, Fastlane
Data & Observability: Kafka, Kinesis, Airflow, Spark, ClickHouse, Redis, ELK, Prometheus
AI/ML: Haystack, LangChain, LlamaIndex, Whisper, YOLO, OpenCV, HuggingFace, ComfyUI, SageMaker
RAG & Vector DBs: Pinecone, Weaviate, Qdrant, OpenSearch (ES)
Product & GTM: SPIN Selling, PMF Discovery, AARRR, Growth Loops, Stripe Connect
R\u00e9sum\u00e9/CV: https://e-nouri.com/cv.pdf\nEmail: nouri [:at:] e-nouri [:dot:] com"},"story_title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["2026"],"value":"Ask HN: Who wants to be hired? (July 2025)"}},"_tags":["comment","author_enouri","story_44434574"],"author":"enouri","comment_text":"Location: Paris, France
Remote: Yes (for flexibility, not a requirement)
Willing to relocate: Yes
I\u2019m Nouri, a senior engineer and architect with 15+ years of experience. I\u2019ve worked for major companies like Intel, Airbus, Canon, CEA (French Atomic Agency), and SNCF, and I\u2019ve also founded and built 11 startups, mostly bootstrapped and focused on SaaS, mobile B2B, and B2C.
I\u2019m now wrapping up my latest startup (no PMF \u2014 product okay but no revenue model): Soir\u00e9es, an AI photo-sharing platform used in 13 countries by 120K+ users, powering events like Paris Fashion Weeks, Euro 2024, and the Paris Olympics 2024. I handled everything from architecture and product to growth, GTM, and AI-powered privacy.
What I\u2019m looking for: a stable, long-term role on a serious product, ideally where I can contribute across architecture, AI, product thinking, and execution. I thrive in roles that blend code, systems, and business alignment.
Technologies
Cloud: AWS (Lambda, API Gateway, DynamoDB, Fargate, SageMaker), GCP, Aliyun, OpenStack
Infra & DevOps: Kubernetes, Docker, Terraform, Serverless Framework, CI/CD
Backend: Python, Node.js, TypeScript, GraphQL, REST, gRPC
Frontend & Mobile: React, React Native, Swift, Kotlin, Fastlane
Data & Observability: Kafka, Kinesis, Airflow, Spark, ClickHouse, Redis, ELK, Prometheus
AI/ML: Haystack, LangChain, LlamaIndex, Whisper, YOLO, OpenCV, HuggingFace, ComfyUI, SageMaker
RAG & Vector DBs: Pinecone, Weaviate, Qdrant, OpenSearch (ES)
Product & GTM: SPIN Selling, PMF Discovery, AARRR, Growth Loops, Stripe Connect
R\u00e9sum\u00e9/CV: https://e-nouri.com/cv.pdf\nEmail: nouri [:at:] e-nouri [:dot:] com","created_at":"2025-07-03T15:04:30Z","created_at_i":1751555070,"objectID":"44455799","parent_id":44434574,"story_id":44434574,"story_title":"Ask HN: Who wants to be hired? (July 2025)","updated_at":"2025-07-03T15:10:55Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"pamelafox"},"comment_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["llamaindex","rag","2026"],"value":"I\u2019ve got this RAG repo working entirely locally (Ollama/Postgres) but it doesnt RAG on documents like you want.
https://github.com/Azure-Samples/rag-postgres-openai-python
I\u2019d like to make that version when I have the time, probably just using Llamaindex for the ingestion.
My tips for getting SLMs working well for RAG:\nhttp://blog.pamelafox.org/2024/08/making-ollama-compatible-r..."},"story_title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["rag"],"value":"Ask HN: Local RAG with private knowledge base"}},"_tags":["comment","author_pamelafox","story_41968366"],"author":"pamelafox","comment_text":"I\u2019ve got this RAG repo working entirely locally (Ollama/Postgres) but it doesnt RAG on documents like you want.
https://github.com/Azure-Samples/rag-postgres-openai-python
I\u2019d like to make that version when I have the time, probably just using Llamaindex for the ingestion.
My tips for getting SLMs working well for RAG:\nhttp://blog.pamelafox.org/2024/08/making-ollama-compatible-r...","created_at":"2024-10-31T00:04:37Z","created_at_i":1730333077,"objectID":"42002033","parent_id":41968366,"story_id":41968366,"story_title":"Ask HN: Local RAG with private knowledge base","updated_at":"2024-11-02T04:48:58Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"behnamoh"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["llamaindex","rag","2026"],"value":"Post ChatGPT, there were many interesting papers on the properties and features of large language models. The pace of publication was so fast that every week we could see at least 1-2 papers that would make the news.
On the open-source side, we had some low quality models and there was so much experimentation to see what works and what doesn't. Soon we got llama and llama 2 and a plethora of models to play with. So many projects started around them, many got abandoned shortly.
Now that I reflect on what happened in 2023, despite all the progress that was made, I feel like the pace of growth has decreased. In early 2023 I legit thought "this is how singularity feels like; every 2-3 days we'll have something new and exciting".
But now things are more settled. We still get new models every day and Mixtral is still amazing. But something seems off. No company (not even Google) was able to make something better than GPT-4. So many Chat UI and wrapper projects are abandoned (Github can be a scary place...), and we're not much further in our way to understand what the heck happens in these models than we were a year ago. In addition, it's become clear that GPT-4-level intelligence might be the best we can extract from the current LLM technology, and no one takes AGI seriously anymore.
Edit: I should add that Langchain, LlamaIndex and so many other "frameworks" built around LLMs that used to be all the rage now are evidently useless in production. RAG is still RAG, and no matter the tricks you play to make it "smarter", it's still just RAG. Langchain and similar frameworks cause more problems than they solve, and the tech debt is horrible. Vector databases are the same. Most are fighting for that sweet VC money, and their features are essentially the same. So many startups suddenly went out of business after OpenAI's first DevDay; so many more will perish after the second DevDay. It's unclear how $$$ VC will be allocated in 2024 given that the safest bet to make money off of AI was and still is OpenAI, not Google, not these third-party frameworks and libraries, not some wrapper around OpenAI's API with a nice UI and shiny website.
What do you think about all this?"},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: Is GenAI hype declining or are low-hanging fruits gone?"}},"_tags":["story","author_behnamoh","story_38898840","ask_hn"],"author":"behnamoh","children":[38898883,38899008,38899149,38899176,38899505,38900855],"created_at":"2024-01-07T05:48:49Z","created_at_i":1704606529,"num_comments":7,"objectID":"38898840","points":11,"story_id":38898840,"story_text":"Post ChatGPT, there were many interesting papers on the properties and features of large language models. The pace of publication was so fast that every week we could see at least 1-2 papers that would make the news.
On the open-source side, we had some low quality models and there was so much experimentation to see what works and what doesn't. Soon we got llama and llama 2 and a plethora of models to play with. So many projects started around them, many got abandoned shortly.
Now that I reflect on what happened in 2023, despite all the progress that was made, I feel like the pace of growth has decreased. In early 2023 I legit thought "this is how singularity feels like; every 2-3 days we'll have something new and exciting".
But now things are more settled. We still get new models every day and Mixtral is still amazing. But something seems off. No company (not even Google) was able to make something better than GPT-4. So many Chat UI and wrapper projects are abandoned (Github can be a scary place...), and we're not much further in our way to understand what the heck happens in these models than we were a year ago. In addition, it's become clear that GPT-4-level intelligence might be the best we can extract from the current LLM technology, and no one takes AGI seriously anymore.
Edit: I should add that Langchain, LlamaIndex and so many other "frameworks" built around LLMs that used to be all the rage now are evidently useless in production. RAG is still RAG, and no matter the tricks you play to make it "smarter", it's still just RAG. Langchain and similar frameworks cause more problems than they solve, and the tech debt is horrible. Vector databases are the same. Most are fighting for that sweet VC money, and their features are essentially the same. So many startups suddenly went out of business after OpenAI's first DevDay; so many more will perish after the second DevDay. It's unclear how $$$ VC will be allocated in 2024 given that the safest bet to make money off of AI was and still is OpenAI, not Google, not these third-party frameworks and libraries, not some wrapper around OpenAI's API with a nice UI and shiny website.
What do you think about all this?","title":"Ask HN: Is GenAI hype declining or are low-hanging fruits gone?","updated_at":"2024-09-20T16:08:14Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"roseway4"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["llamaindex","rag","2026"],"value":"Hi HN, we're Daniel, Paul, Travis, and Preston from Zep. We\u2019ve just open-sourced Zep Community Edition, a memory layer for AI agents that continuously learns facts from user interactions and changing business data. Zep ensures that your Agent has the knowledge needed to accomplish tasks successfully.
Github: https://github.com/getzep/zep
Zep walkthrough: https://vimeo.com/1013045013
A few weeks ago, we shared Graphiti, our library for building temporal Knowledge Graphs (https://news.ycombinator.com/item?id=41445445). Zep runs Graphiti under the hood, progressively building and updating a temporal graph from chat interactions, tool use, and business data in JSON or unstructured text.
Zep allows you to build personalized and more accurate user experiences. With increased LLM context lengths, including the entire chat history, RAG results, and other instructions in a prompt can be tempting. We\u2019ve experienced poor temporal reasoning and recall, hallucinations, and slow and expensive inference when doing so.
We believe temporal graphs are the most expressive and dense structure for modeling an agent\u2019s dynamic world (changing user preferences, traits, business data etc). We took inspiration from projects such as MemGPT but found that agent-powered retrieval and complex multi-level architectures are slow, non-deterministic, and difficult to reason with. Zep\u2019s approach, which asynchronously precomputes the graph and related facts, supports very low-latency, deterministic retrieval.
Here\u2019s how Zep works, from adding memories to organizing the graph:
1. Zep identifies nodes and relationships in chat messages or business data. You can specify if new entities should be added to a user and/or group of users.
2. The graph is searched for similar existing nodes. Zep deduplicates new nodes and edge types, ensuring orderly ontology growth.
3. Temporal information is extracted from various sources like chat timestamps, JSON date fields, or article publication dates.
4. New nodes and edges are added to the graph with temporal metadata.
5. Temporal data is reasoned with, and existing edges are updated if no longer valid. More below.
6. Natural language facts are generated for each edge and embedded for semantic and full-text search.
Zep retrieves facts by examining recent user data and combining semantic, BM25, and graph search methods. One technique we\u2019ve found helpful is reranking semantic and full-text results by distance from a user node.
Zep is framework agnostic and can be used with LangChain, LangGraph, LlamaIndex, or without a framework. SDKs for Python, TypeScript, and Go are available.
# More about how Zep manages state changes
Zep reconciles changes in facts as the agent\u2019s environment changes. We use temporal metadata on graph edges to track fact validity, allowing agents to reason with these state changes:
Fact: \u201cKendra loves Adidas shoes\u201d (valid_at: 2024-08-10)
User message: \u201cI\u2019m so angry! My favorite Adidas shoes fell apart! Puma\u2019s are my new favorite shoes!\u201d (2024-09-25)
Facts:
- \u201cKendra loves Adidas shoes.\u201d (valid_at: 2024-08-10, invalid_at: 2024-09-25)
- \u201cKendra\u2019s Adidas shoes fell apart.\u201d (valid_at: 2024-09-25)
- \u201cKendra prefers Puma.\u201d (valid_at: 2024-09-25)
You can read more about Graphiti\u2019s design here: https://blog.getzep.com/llm-rag-knowledge-graphs-faster-and-...
Zep Community Edition is released under the Apache Software License v2. We\u2019ll be launching a commercial version of Zep soon, which like Zep Community Edition, builds a graph of an agent\u2019s world.
Zep on GitHub: https://github.com/getzep/zep
Quick Start: https://help.getzep.com/ce/quickstart
Key Concepts: https://help.getzep.com/concepts
SDKs: https://help.getzep.com/ce/sdks
Let us know what you think! We\u2019d love your thoughts, feedback, bug reports, and/or contributions!"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Zep \u2013 Open-Source Graph Memory for AI Apps"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://blog.getzep.com/announcing-zep-community-edition/"}},"_tags":["story","author_roseway4","story_41660503","show_hn"],"author":"roseway4","children":[41660896],"created_at":"2024-09-26T16:40:33Z","created_at_i":1727368833,"num_comments":0,"objectID":"41660503","points":6,"story_id":41660503,"story_text":"Hi HN, we're Daniel, Paul, Travis, and Preston from Zep. We\u2019ve just open-sourced Zep Community Edition, a memory layer for AI agents that continuously learns facts from user interactions and changing business data. Zep ensures that your Agent has the knowledge needed to accomplish tasks successfully.
Github: https://github.com/getzep/zep
Zep walkthrough: https://vimeo.com/1013045013
A few weeks ago, we shared Graphiti, our library for building temporal Knowledge Graphs (https://news.ycombinator.com/item?id=41445445). Zep runs Graphiti under the hood, progressively building and updating a temporal graph from chat interactions, tool use, and business data in JSON or unstructured text.
Zep allows you to build personalized and more accurate user experiences. With increased LLM context lengths, including the entire chat history, RAG results, and other instructions in a prompt can be tempting. We\u2019ve experienced poor temporal reasoning and recall, hallucinations, and slow and expensive inference when doing so.
We believe temporal graphs are the most expressive and dense structure for modeling an agent\u2019s dynamic world (changing user preferences, traits, business data etc). We took inspiration from projects such as MemGPT but found that agent-powered retrieval and complex multi-level architectures are slow, non-deterministic, and difficult to reason with. Zep\u2019s approach, which asynchronously precomputes the graph and related facts, supports very low-latency, deterministic retrieval.
Here\u2019s how Zep works, from adding memories to organizing the graph:
1. Zep identifies nodes and relationships in chat messages or business data. You can specify if new entities should be added to a user and/or group of users.
2. The graph is searched for similar existing nodes. Zep deduplicates new nodes and edge types, ensuring orderly ontology growth.
3. Temporal information is extracted from various sources like chat timestamps, JSON date fields, or article publication dates.
4. New nodes and edges are added to the graph with temporal metadata.
5. Temporal data is reasoned with, and existing edges are updated if no longer valid. More below.
6. Natural language facts are generated for each edge and embedded for semantic and full-text search.
Zep retrieves facts by examining recent user data and combining semantic, BM25, and graph search methods. One technique we\u2019ve found helpful is reranking semantic and full-text results by distance from a user node.
Zep is framework agnostic and can be used with LangChain, LangGraph, LlamaIndex, or without a framework. SDKs for Python, TypeScript, and Go are available.
# More about how Zep manages state changes
Zep reconciles changes in facts as the agent\u2019s environment changes. We use temporal metadata on graph edges to track fact validity, allowing agents to reason with these state changes:
Fact: \u201cKendra loves Adidas shoes\u201d (valid_at: 2024-08-10)
User message: \u201cI\u2019m so angry! My favorite Adidas shoes fell apart! Puma\u2019s are my new favorite shoes!\u201d (2024-09-25)
Facts:
- \u201cKendra loves Adidas shoes.\u201d (valid_at: 2024-08-10, invalid_at: 2024-09-25)
- \u201cKendra\u2019s Adidas shoes fell apart.\u201d (valid_at: 2024-09-25)
- \u201cKendra prefers Puma.\u201d (valid_at: 2024-09-25)
You can read more about Graphiti\u2019s design here: https://blog.getzep.com/llm-rag-knowledge-graphs-faster-and-...
Zep Community Edition is released under the Apache Software License v2. We\u2019ll be launching a commercial version of Zep soon, which like Zep Community Edition, builds a graph of an agent\u2019s world.
Zep on GitHub: https://github.com/getzep/zep
Quick Start: https://help.getzep.com/ce/quickstart
Key Concepts: https://help.getzep.com/concepts
SDKs: https://help.getzep.com/ce/sdks
Let us know what you think! We\u2019d love your thoughts, feedback, bug reports, and/or contributions!","title":"Show HN: Zep \u2013 Open-Source Graph Memory for AI Apps","updated_at":"2024-10-08T20:03:54Z","url":"https://blog.getzep.com/announcing-zep-community-edition/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"ksaimanikanta45"},"comment_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["llamaindex","rag","2026"],"value":"SEEKING | AI/ML Engineer | Auburn AL \u2192 SF/Seattle/NYC/Remote | \nF-1 STEM OPT through Feb 2028 (no sponsorship needed)
4 yrs production GenAI: AWS Bedrock RAG (92% recall, HIPAA), \nLangGraph multi-agent copilots shipped to prod, QLoRA/LLaMA 2 \nfine-tuning, LangChain/LlamaIndex, Python, Docker/K8s.
Co-author ACL 2024 (healthcare AI). MS Data Science UNT 2024.\nCurrently ML Research Scientist @ Auburn (multi-agent VLMs).
Open to: Applied Scientist / ML Eng / GenAI Eng / AI Eng
Email: ksaimanikanta4@gmail.com\nGitHub: github.com/saikasireddy\nResume: [https://docs.google.com/document/d/1pjp-I0XfRP96fxu-nbIdOAVQ...]"},"story_title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["2026"],"value":"Ask HN: Who is hiring? (April 2026)"}},"_tags":["comment","author_ksaimanikanta45","story_47601859"],"author":"ksaimanikanta45","comment_text":"SEEKING | AI/ML Engineer | Auburn AL \u2192 SF/Seattle/NYC/Remote | \nF-1 STEM OPT through Feb 2028 (no sponsorship needed)
4 yrs production GenAI: AWS Bedrock RAG (92% recall, HIPAA), \nLangGraph multi-agent copilots shipped to prod, QLoRA/LLaMA 2 \nfine-tuning, LangChain/LlamaIndex, Python, Docker/K8s.
Co-author ACL 2024 (healthcare AI). MS Data Science UNT 2024.\nCurrently ML Research Scientist @ Auburn (multi-agent VLMs).
Open to: Applied Scientist / ML Eng / GenAI Eng / AI Eng
Email: ksaimanikanta4@gmail.com\nGitHub: github.com/saikasireddy\nResume: [https://docs.google.com/document/d/1pjp-I0XfRP96fxu-nbIdOAVQ...]","created_at":"2026-04-14T00:19:37Z","created_at_i":1776125977,"objectID":"47759666","parent_id":47601859,"story_id":47601859,"story_title":"Ask HN: Who is hiring? (April 2026)","updated_at":"2026-04-14T00:21:20Z"}],"hitsPerPage":20,"nbHits":7,"nbPages":1,"page":0,"params":"query=llamaindex+rag+2026&advancedSyntax=true&analyticsTags=backend","processingTimeMS":17,"processingTimingsMS":{"_request":{"roundTrip":22},"afterFetch":{"format":{"highlighting":1,"total":1},"merge":{"total":2},"total":2},"fetch":{"query":13,"scanning":1,"total":15},"total":17},"query":"llamaindex rag 2026","serverTimeMS":19}