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Typefully
@typefully
We're launching a Command Bar today with great commands and features.
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@aaditsh
I think this thread hook could be improved.
@frankdilo
On it 🔥
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Easlo
@heyeaslo
Reply with "Notion" to get early access to my new template.
Jaga
@kandros5591
Notion 🙏
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Tweeting more with @typefully these days.
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Jurre Houtkamp@jurrehoutkamp
Typefully is fantastic and way too cheap for what you get.
We’ve tried many alternatives at @framer but nothing beats it. If you’re still tweeting from Twitter you’re wasting time.
DHH@dhh
This is my new go-to writing environment for Twitter threads.
They've built something wonderfully simple and distraction free with Typefully 😍
Santiago@svpino
For 24 months, I tried almost a dozen Twitter scheduling tools.
Then I found @typefully, and I've been using it for seven months straight.
When it comes down to the experience of scheduling and long-form content writing, Typefully is in a league of its own.
Luca Rossi ꩜@lucaronin
After trying literally all the major Twitter scheduling tools, I settled with @typefully.
Killer feature to me is the native image editor — unique and super useful 🙏
Visual Theory@visualtheory_
Really impressed by the way @typefully has simplified my Twitter writing + scheduling/publishing experience.
Beautiful user experience.
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@aaditsh
I think this thread hook could be improved.
@frankdilo
On it 🔥
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Question and answering applications are the most useful applications of generative AI
Let's look at this most promising use-case of generative AI👇
What is a Q&A application using LLMs?
A question and answering (Q&A) application using large language models involves utilizing advanced natural language processing techniques, particularly those offered by large language models like GPT-3.5.
learn more: bit.ly/47H9DL6
These applications aim to automatically generate accurate and relevant answers to user-provided questions in a human-like manner.
This approach is flexible and far better than fine-tuning custom LLM because this is much more cost-effective than letter one.
Q&A application workflow:
1. Document loaders: To load user documents for vectorization and storage purposes
2. Text splitters: These are the document transformers that transform documents into fixed chunk lengths to store them efficiently
3. Vector storage: Vector database integrations to store vector embeddings of the input texts
4. Document retrieval: To retrieve texts based on user queries to the database. They use similarity search techniques to retrieve the same.
5. Model output: Final model output to the user query generated from the input prompt of query and retrieved texts.
Depiction of an entire workflow👇
Advantages of Custom Q&A Applications Over a Model Fine-tuning
1. Context-specific answers
2. Adaptable to new input documents
3. No need to fine-tune the model, which saves the cost of model training
4. More accurate and specific answers rather than general answers