Vana Reviews

1. Volodymyr

   · Working at Hata · over 2 years ago
   Volodymyr

   This app brings me joy by enabling the creation of a virtual self.

   👍 Pros:    Well designed|Powerful
2. Taharob

   over 2 years ago
   Excellent vana

   I'm thrilled to share my thoughts on Vana after experiencing it firsthand. This app is truly remarkable and sets a new standard in innovation. The way it integrates your personality traits into a mini version of yourself adds a captivating dimension to the overall user experience. Seeing the mini version of yourself engage in conversations and exhibit behavior similar to yours is both entertaining and thought-provoking. It provides a distinct opportunity for self-reflection as you witness how your traits are portrayed in digital form.

3. Sabbir

   · Manager at null · over 2 years ago
   Vana: My Ideal Digital Twin

   All is good

   🏁 Competitors: Zendesk

   👍 Pros:    Create a personalized mini-you ("gotchi"): craft a unique digital twin mirroring your voice, appearance, and data for a personalized self-exploration journey. tailored interactions: receive customized games, simulations, and ai assistant tools based on your gotchi's characteristics, enhancing the user experience. self-discovery: engage in meaningful conversations and explore diverse facets of yourself through interactive sessions with your gotchi.

   👎 Cons:    Ethical considerations: address potential privacy concerns and biases in the ai development process to ensure responsible and ethical use. unclear target audience: the broad appeal to "everyone" may necessitate clearer value propositions for different user groups, enhancing market relevance and engagement.

GitHub Reviews

1. Reinhard

   · Boss at CLOUD Meister · over 5 years ago
   perfect 4 open Source

Vana mentions (0)

GitHub mentions (2474)

• I audited a 5.5 GB AI training dataset by downloading 0.8% of it

  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 / 4 days ago

• Superlogical – Mitchell Hashimoto

  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:

          Host *.

  - Source: Hacker News / 12 days ago

• Your Agent's Confidence Score Is Not a Probability

  All of this assumes you can actually inspect what the agent did — 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 / 12 days ago

• Foreman 101: agentic coding as Kubernetes resources

  # 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 / 13 days ago

• Stop Judging Every Run: Eval Sampling Is a Budget Decision, Not a Coverage One

  This is why eval and observability ship as a unit, not as separate purchases. agent-eval scores and gates the output — 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 / 22 days ago