Matthias Plappert matthiasplappert

id	idea	low_token_cost	signal_speed	agent_demand	low_cash_cost	total	state	reason
I-001	Grant change intelligence	5	5	5	5	20	selected	One buyer made 30 paid calls and longitudinal source changes can create owned data
I-002	OSHA inspection and citation search	4	4	4	5	17	building	The official search returned current normalized inspection records without source authentication
I-003	USPTO patent status search	3	3	4	5	15	portfolio	Agents need sourced status data before product and legal work
I-004	FCC license status search	4	4	3	5	16	candidate	Official license data supports telecom and vendor checks
I-005	US and EU food safety alert feed	3	4	4	5	16	discarded	Recall products already cover much of this need
I-006	Live electricity carbon-intensity API	2	4	4	2	12	discarded	Reliable global source access can require paid data
I-007	Port and shipping disruption feed	2	4	4	2	12	discarded	Reliable live source access can require paid data
I-008	State insurance bulletin feed	3	3	3	5	14	discarded	A listed x402 product already

This script automatically sets up my vim environment on any machine. For example in a docker container or an EC2 instance.

Dependencies

I hereby claim:

I am matthiasplappert on github.
I am mplappert (https://keybase.io/mplappert) on keybase.
I have a public key whose fingerprint is BC81 5B1C 87B4 4B43 9C1D 3811 20F2 600F 217E C448

To claim this, I am signing this object:

	import numpy as np
	import gym


	env = gym.make('FetchReach-v0')

	# Simply wrap the goal-based environment using FlattenDictWrapper
	# and specify the keys that you would like to use.
	env = gym.wrappers.FlattenDictWrapper(
	env, dict_keys=['observation', 'desired_goal'])

	import numpy as np
	import gym


	env = gym.make('FetchReach-v0')
	obs = env.reset()
	done = False

	def policy(observation, desired_goal):
	# Here you would implement your smarter policy. In this case,

	from keras import backend as K

	actor = None # the following code assumes that actor and critic are Graph networks
	critic = None
	action_input_name = 'input_action'
	output_name = 'output'
	batch_size = 64

	# Temporarily connect to a large, combined model so that we can compute the gradient and monitor
	# the performance of the actor as evaluated by the critic.

	@interface UIView (MPAdditions)
	@end

	@implementation UIView (MPAdditions)

	- (id)debugQuickLookObject {
	if (self.bounds.size.width < 0.0f \|\| self.bounds.size.height < 0.0f) {
	return nil;
	}

	import java.io.File;
	import java.io.IOException;

	public final class Test {
	private Test() {

	}

	public static void main(String[] args) {
	try {

	#!/bin/sh
	java -classpath your/path/to/checkstyle/checkstyle-5.5/checkstyle-5.5-all.jar com.puppycrawl.tools.checkstyle.Main -c your/path/to/checkstyle_swt1.xml -r src/