Nicolay Gerold
@nicolaygerold
Nicolay Gerold
Startups and
We call it WINS Work: the places where tasks, functions, possibly your entire company or industry are dependent on the manipulation and interpretation of Words, Images, Numbers, and Sounds (WINS). Heart surgeons and chefs are knowledge workers but not WINS workers. Software programmers, accountants, and marketing professionals are WINS workers.
GenAI has the potential to be power tools for WINS work. Every WINS task, subprocess, and end-to-end process within your enterprise (and in many cases the entire enterprise) should be evaluated for potential leverage with GenAI.
Plotting your company on a 2×2 matrix, ask yourself where it falls. The top-level categorization can be accomplished by looking at the cost base of the firm overall and then by function, and estimating the percentage of work that is WINS work. For example, software development, customer service, marketing, and R&D are just four areas with high levels of WINS work.
What is Hatchet?
Hatchet replaces difficult to manage legacy queues or pub/sub systems so you can design durable workloads that recover from failure and solve for problems like concurrency , fairness , and rate limiting . Instead of managing your own task queue or pub/sub system, you can use Hatchet to distribute your functions between a set of... See more
Hatchet replaces difficult to manage legacy queues or pub/sub systems so you can design durable workloads that recover from failure and solve for problems like concurrency , fairness , and rate limiting . Instead of managing your own task queue or pub/sub system, you can use Hatchet to distribute your functions between a set of... See more
hatchet-dev • GitHub - hatchet-dev/hatchet: A distributed, fault-tolerant task queue
Cross-Encoder for Hallucination Detection
This model was trained using SentenceTransformers Cross-Encoder class.
The model outputs a probabilitity from 0 to 1, 0 being a hallucination and 1 being factually consistent.
The predictions can be thresholded at 0.5 to predict whether a document is consistent with its source.
Training Data
This model is... See more
This model was trained using SentenceTransformers Cross-Encoder class.
The model outputs a probabilitity from 0 to 1, 0 being a hallucination and 1 being factually consistent.
The predictions can be thresholded at 0.5 to predict whether a document is consistent with its source.
Training Data
This model is... See more
vectara/hallucination_evaluation_model · Hugging Face
The microbiome can be steered through the ingestion of certain foods.
Andrew Huberman • How Foods and Nutrients Control Our Moods
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.
Idempotency is a concept that is important for programmers to understand, especially those working on building distributed systems. In simple terms, idempotency means that if you perform an operation multiple times, the end result should be the same as if you had only performed it once.
In other words, an idempotent operation is one that can be... See more
In other words, an idempotent operation is one that can be... See more
Every Programmer Should Know #1: Idempotency
Simply, it is possible to make a POST request idempotent by including a unique identifier in the request body or header, which can be used to identify and prevent duplicate requests.
HeimdaLLM
Pronounced [ˈhaɪm.dɔl.əm] or HEIM-dall-EM
HeimdaLLM is a robust static analysis framework for validating that LLM-generated structured output is safe. It currently supports SQL.
In simple terms, it helps makes sure that AI won't wreck your systems.
Pronounced [ˈhaɪm.dɔl.əm] or HEIM-dall-EM
HeimdaLLM is a robust static analysis framework for validating that LLM-generated structured output is safe. It currently supports SQL.
In simple terms, it helps makes sure that AI won't wreck your systems.
amoffat • GitHub - amoffat/HeimdaLLM: Verify LLM output
how can you take the knowledge work that someone is doing and use AI to help them be dramatically more productive at doing that particular flavor of cognitive work? In our observation with developers, more than anything else, AI helps keep them in flow state longer than they otherwise would. Rather than hitting a blocker when you’re writing a chunk... See more