Distill is a web monitoring tool. It can monitor RSS feeds, a webpage or a part of webpage. Alerts in the form of pop-up, audio or emails can be received.
Featured highlights:
• Content selection from any webpage including iframes. • Supported actions: • Push notifications on phone • Pop-up • Audio • Email • SMS • Webhook • Easy to use inbox style Watchlist with label, trash etc. • Conditions for smart actions. • History of previous changes for each monitor. • Powerful expressions to select source content. Supports XPath expression, CSS rule or JavaScript expression to select content. • View highlighted changes.
One of the best tools I have used to keep track of changes on my competitors website. I love the chrome extension and its ease of use.
Based on our record, Jsonformer should be more popular than Distill Web Monitor. It has been mentiond 9 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
I haven't used any in recent years so I'm not up to speed with the pros/cons of each, but PageProbe, SiteDelta Watch, and Distill Web Monitor are a few to try. Source: about 2 years ago
I’ve had pretty good luck with the distill web monitor https://distill.io/apps/web-monitor. Source: about 3 years ago
How does this compare in terms of latency, cost, and effectiveness to jsonformer? https://github.com/1rgs/jsonformer. - Source: Hacker News / 10 months ago
I'm not sure how this is different than: https://github.com/1rgs/jsonformer or https://github.com/mkuchnik/relm or https://github.com/Shopify/torch-grammar Overall there are a ton of these logit based guidance systems, the reason they don't get tons of traction is the SOTA models are behind REST APIs that don't enable this fine-grained approach. Those... - Source: Hacker News / 10 months ago
You're correct with interpreting how the model works wrt it returning tokens one at a time. The model returns one token, and the entire context window gets shifted right by one to for account it when generating the next one. As for model performance at different context sizes, it's seems a bit complicated. From what I understand, even if models are tweaked (for example using the superHOT RoPE hack or sparse... - Source: Hacker News / 10 months ago
From here, we just need to continue generating tokens until we get to a closing quote. This approach was borrowed from Jsonformer which uses a similar approach to induce LLMs to generate structured output. Continuing to do so for each property using Replit's code LLM gives the following output:. - Source: dev.to / 11 months ago
Https://github.com/1rgs/jsonformer or https://github.com/microsoft/guidance may help get better results, but I ended up with a bit more of a custom solution. Source: 12 months ago
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