Nicolay Gerold
@nicolaygerold
Nicolay Gerold
Second, TA teaches us to never overlook the simplest solutions. There are a lot of complicated methods to automatically find augmentation policies, but the simplest method was so-far overlooked , even though it performs comparably or better
Devansh • TrivialAugment: The next evolution in Data Augmentation [Breakdowns]
https://github.com/huggingface/chat-ui - Amazing clean UI with very good web search, my go to currently. (they added the ability to do it all locally very recently!)
https://github.com/oobabooga/text-generation-webui - Best overall, supports any model format and has many extensions
https://github.com/ParisNeo/lollms-webui/ - Has PDF, stable diffusion... See more
https://github.com/oobabooga/text-generation-webui - Best overall, supports any model format and has many extensions
https://github.com/ParisNeo/lollms-webui/ - Has PDF, stable diffusion... See more
r/LocalLLaMA - Reddit
Always drill down deeper once you feel frustrated. The adult brain cannot cope with large errors, but only with smaller incremental ones. You have to stack the errors to achieve plasticity. Smaller bouts and bits of learning are necessary per learning epoch.
Andrew Huberman • Using Failures, Movement & Balance to Learn Faster
All of the effort spent deliberating on edge cases and long tails stems from the fact that many junior devs are not actually thinking hard enough about what the experiment should be, and what the metrics should look like.
The goal of building out these probabilistic software systems is not a milestone or a feature. Instead, what we're looking for... See more
The goal of building out these probabilistic software systems is not a milestone or a feature. Instead, what we're looking for... See more
Jason Liu • Tips for probabilistic software - jxnl.co
Build an optimized, secure, and frontend-independent application for multi-platform deployment.
Build smaller, faster, and more secure desktop applications with a web frontend | Tauri Apps
Data Transformation
Built For Growth
Don't hack custom scripts or use half-baked tools. SQLMesh ensures accurate and efficient data pipelines with the most complete DataOps solution for transformation, testing, and collaboration.
Built For Growth
Don't hack custom scripts or use half-baked tools. SQLMesh ensures accurate and efficient data pipelines with the most complete DataOps solution for transformation, testing, and collaboration.
SQLMesh
SkyPilot is a framework for running LLMs, AI, and batch jobs on any cloud, offering maximum cost savings, highest GPU availability, and managed execution.
SkyPilot abstracts away cloud infra burdens :
SkyPilot... See more
SkyPilot abstracts away cloud infra burdens :
- Launch jobs & clusters on any cloud
- Easy scale-out: queue and run many jobs, automatically managed
- Easy access to object stores (S3, GCS, R2)
SkyPilot... See more
skypilot-org • GitHub - skypilot-org/skypilot: SkyPilot: Run LLMs, AI, and Batch jobs on any cloud. Get maximum savings, highest GPU availability, and managed execution—all with a simple interface.
Classwords are suffixes added to database column names to indicate the type of data they contain. This improves readability and makes it easier to understand the database schema. Base classwords include text, calendar, numeric and domain-specific types. It is best to avoid redundancy in column names, as this can lead to unnecessary verbosity. Using... See more
Gemini - chat to supercharge your ideas
Text Classwords
identifier (or id)
code[_<standard>]
name
description (or desc)
indicator (or ind)
number
text
Calendar Classwords
date
datetime[<timezone>] (or dt[<timezone>])
timestamp[<timezone>] (or ts[<timezone>])
Numeric Classwords
count
amount[_<currency>]
<quantity_property>[_<unit_of_measure>]
ratio
factor
percent (or pct)
Domain-Specific Classwords
uri
address
email
sku
json
geojson
Analyze user queries and feedback to identify topic clusters, capabilities, and areas of user dissatisfaction. This will help you prioritize improvements.
Why should we do this? Let me give you an example. I once worked with a company that provided a technical documentation search system. By clustering user queries, we identified two main issues:
Why should we do this? Let me give you an example. I once worked with a company that provided a technical documentation search system. By clustering user queries, we identified two main issues:
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