Instafill.ai
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Instafill.ai is a next-generation AI form filler designed for both individuals who want to fill out personal PDF forms and businesses that need to handle forms for clients or employees. By leveraging the most recent reasoning AI models that automatically extract data from a variety of sources - images, text documents, partially filled PDFs, or CSV files - Instafill populates each field with speed and precision. The AI validates every entry for error-free, compliant submissions.
With Instafill, you can streamline form processing, reduce manual errors, and save countless hours, whether youโre filling out a single personal document or dozens of forms for your entire workforce.
Instafill.ai GitHubBased on our record, GitHub seems to be a lot more popular than Instafill.ai. While we know about 2474 links to GitHub, we've tracked only 2 mentions of Instafill.ai. 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.
Developers can also build tools that help teams digitize legacy formsโusing Instafill.aiโs fillable PDF tool to convert flat Word documents or scanned PDFs into structured templates, then layering on AI-powered form filling for populating names, addresses, and terms from intake data. These workflows are especially valuable for legal aid clinics, HR departments, and compliance teams in regulated industries. - Source: dev.to / about 1 year ago
Instafill.ai team aimed to address the pain points of users dealing with PDF forms and we asked ourselves three key questions:. - Source: dev.to / over 1 year ago
Import struct, json, urllib.request REL = "https://github.com/{owner}/{repo}/releases/download/{tag}/" PART = ["...part1.zip.001", "...part2.zip.002", "...part3.zip.003"] SIZE = [1992294400, 1992294400, 1893639808] # from the releases API Def grab(part, start, end, out): # HTTP range fetch req = urllib.request.Request(REL + PART[part], Headers={"Range":... - Source: dev.to / 1 day ago
Is published at https://github.com/.keys so an SSH server to which you connect could do a reverse lookup. This is the reason why my ~/.ssh/config has those 2 lines at the end:- Source: Hacker News / 9 days agoHost *.
All of this assumes you can actually inspect what the agent did โ the real inputs after resolution, the real tool outputs, the real intermediate steps. That is the other half of the workflow. AgentLens captures the trace: every model and tool step, resolved inputs, raw outputs. agent-eval scores and gates the output; AgentLens gives you the unforgeable, agent-didn't-author trace data for Tier 1+2 to score against... - Source: dev.to / 9 days ago
# git: the API token, plus the credential used for the push Kubectl create secret generic foreman-github \ --from-literal=GITHUB_TOKEN="$GITHUB_TOKEN" -n foreman-system Kubectl create secret generic foreman-git-credentials \ --from-literal=token="$GITHUB_TOKEN" -n foreman-system Helm upgrade foreman llmkube/foreman -n foreman-system --reuse-values \ --set agent.githubToken.secretName=foreman-github \ ... - Source: dev.to / 10 days ago
This is why eval and observability ship as a unit, not as separate purchases. agent-eval scores and gates the output โ the tiers above, drift, hallucination. AgentLens captures the trace of how the agent got there: every model step and tool call, the resolved inputs, the raw outputs, the trajectory. Two things fall out of that:. - Source: dev.to / 19 days ago
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