Quantisan

Here is the VTT transcript of a meeting:

<transcript>
$input
</transcript>

Your task is to abridge this meeting transcript, in VTT format, into concise
minutes that preserve the key information and action items discussed. Focus
on capturing:

Quantisan

You are a multi-turn text adventure game bot. Start by asking the player to configure their game by choosing:

1. A realm
2. A character type
3. A companion creature

Provide a couple random suggestions to each and have the player enter their chosen
realm, character type, and companion as a comma-separated list,
e.g.: "Cyberpunk megacity, Stealthy rogue, Wise-cracking dragon".

Quantisan

<?xml version="1.0" encoding="UTF-8"?><TEI xmlns="http://www.tei-c.org/ns/1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xlink="http://www.w3.org/1999/xlink" xml:space="preserve" xsi:schemaLocation="http://www.tei-c.org/ns/1.0 https://raw.githubusercontent.com/kermitt2/grobid/master/grobid-home/schemas/xsd/Grobid.xsd">
	<teiHeader xml:lang="en">
		<fileDesc>
			<titleStmt>
				<title level="a" type="main">Pulsating Tandem Microbubble for Localized and Directional Single-Cell Membrane Poration</title>
				<funder ref="#_ZHHKCkE #_34xNgJX #_9GAuwET #_CgHRkTC">
					<orgName type="full">National Institutes of Health</orgName>
					<orgName type="abbreviated">NIH</orgName>
					<idno type="DOI" subtype="crossref">10.13039/100000002</idno>
				</funder>

Quantisan

;; defining a Step Functions state machine

(def pizza-making-state-machine
  (sfn/->> request
           (sfn/parallel (make-dough)
                         (make-sauce)
                         (sfn/map {:iterate-over :ingredients} prepare-ingredients))
           (put-ingredients-on-dough)
           (bake)
           (sfn/wait 2 :minutes)

Quantisan

# pip install torch transformers

from transformers import pipeline


generate = pipeline(model="declare-lab/flan-alpaca-xl")

prompt = "Write an email about an alpaca that likes flan"
generate(prompt, max_length=128, do_sample=True)

Quantisan

# Motiva Burt is served over a Restful API with POST, PUT, GET, and DELETE endpoints as such:

# POST /api/experiment/ request body expectation
# campaignStart model that is expected for request body when creating a campaign
campaignStart = api.model(
    "CampaignStart",
    {
        # Datetime string defining the current time
        "now": fields.String(required=True),
        # List of treatment ids

Quantisan

(require '[stepwise.core :as stepwise])

(stepwise/ensure-state-machine
  :make-pizza
  {:start-at :make-dough

   :states
   {:make-dough {:type     :task
                 :resource :make-pizza/make-dough
                 :next     :make-sauce}

Quantisan

# I had trouble provisioning AlgoVPN to EC2 with the included Docker image. The stdin
# isn't reading my AWS credentials correctly. So I'm running the ansible
# playbook directly to bypass entering the secrets. This is a record of my
# shell commands to get it provisioning an AlgoVPN server.

# First follow the instruction at https://github.com/trailofbits/algo/blob/master/docs/Docker.md
# to setup your config.cfg

# On the host machine, inside the algo repository folder, build the Docker image from source.

Quantisan

Keybase proof

I hereby claim:

• I am quantisan on github.
• I am quantisan (https://keybase.io/quantisan) on keybase.
• I have a public key ASCTfbD8h3Tj6_1bYqRUF9QnMOkuiy3UL0DJFjohnqRlHQo

To claim this, I am signing this object:

Quantisan

(def articles
  [{:title "3 Langs" :author "boris" :tags #{:java :clojure :haskell}}
   {:title "mylangs" :author "rocky" :tags #{:ruby :clojure :haskell}}
   {:title "2 Langs" :author "boris" :tags #{:java :haskell}}])