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GitHub Copilot for CLI for PowerShell

Software Development

codesanitize
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20 August 2024
0

GitHub Subsequent has this cool challenge that’s mainly Copilot for the CLI (command line interface). You’ll be able to join their waitlist on the Copilot for CLI website.

Copilot for CLI offers three shell instructions: ??, git? and gh?

That is cool and all, however I exploit PowerShell. Seems these ?? instructions are simply router instructions to a bigger EXE known as github-copilot-cli. So in case you go “?? one thing” you are actually going “github-copilot-cli what-the-shell one thing.”

So this implies I ought to have the ability to to do the identical/comparable aliases for my PowerShell immediate AND change the injected immediate (have a look at me I am a immediate engineer) so as to add ‘use powershell to.’

Now it isn’t good, however hopefully it’s going to make the purpose to the Copilot CLI crew that PowerShell wants love additionally.

Listed here are my aliases. Be happy to recommend if these suck. Word the addition of “person powershell to” for the ?? one. I’ll make a ?? and a p? the place one does bash and one does PowerShell. I may even have it use wsl.exe and shell out to bash. A lot of prospects.

perform ?? {
$TmpFile = New-TemporaryFile
github-copilot-cli what-the-shell ('use powershell to ' + $args) --shellout $TmpFile
if ([System.IO.File]::Exists($TmpFile)) {
$TmpFileContents = Get-Content material $TmpFile
if ($TmpFileContents -ne $nill) {
Invoke-Expression $TmpFileContents
Take away-Merchandise $TmpFile
}
}
}
perform git? {
$TmpFile = New-TemporaryFile
github-copilot-cli git-assist $args --shellout $TmpFile
if ([System.IO.File]::Exists($TmpFile)) {
$TmpFileContents = Get-Content material $TmpFile
if ($TmpFileContents -ne $nill) {
Invoke-Expression $TmpFileContents
Take away-Merchandise $TmpFile
}
}
}
perform gh? {
$TmpFile = New-TemporaryFile
github-copilot-cli gh-assist $args --shellout $TmpFile
if ([System.IO.File]::Exists($TmpFile)) {
$TmpFileContents = Get-Content material $TmpFile
if ($TmpFileContents -ne $nill) {
Invoke-Expression $TmpFileContents
Take away-Merchandise $TmpFile
}
}
}

It additionally then affords to run the command. Very clean.

Hope you prefer it. A lot of enjoyable stuff taking place on this area.

About Scott

Scott Hanselman is a former professor, former Chief Architect in finance, now speaker, guide, father, diabetic, and Microsoft worker. He’s a failed stand-up comedian, a cornrower, and a guide creator.

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Chat with AI in RStudio

Artificial Intelligence

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20 August 2024
0

chattr is a bundle that permits interplay with Massive Language Fashions (LLMs),
akin to GitHub Copilot Chat, and OpenAI’s GPT 3.5 and 4. The principle car is a
Shiny app that runs contained in the RStudio IDE. Right here is an instance of what it seems to be
like working contained in the Viewer pane:

Determine 1: chattr’s Shiny app

Although this text highlights chattr’s integration with the RStudio IDE,
it’s price mentioning that it really works exterior RStudio, for instance the terminal.

Getting began

To get began, set up the bundle from CRAN, after which name the Shiny app
utilizing the chattr_app() operate:

# Set up from CRAN set up.packages("chattr") # Run the app chattr::chattr_app() #> ── chattr - Accessible fashions #> Choose the variety of the mannequin you want to use: #> #> 1: GitHub - Copilot Chat - (copilot) #> #> 2: OpenAI - Chat Completions - gpt-3.5-turbo (gpt35) #> #> 3: OpenAI - Chat Completions - gpt-4 (gpt4) #> #> 4: LlamaGPT - ~/ggml-gpt4all-j-v1.3-groovy.bin (llamagpt) #> #> #> Choice: >

After you choose the mannequin you want to work together with, the app will open. The
following screenshot offers an summary of the totally different buttons and
keyboard shortcuts you should utilize with the app:

Determine 2: chattr’s UI

You can begin writing your requests in the principle textual content field on the high left of the
app. Then submit your query by both clicking on the ‘Submit’ button, or
by urgent Shift+Enter.

chattr parses the output of the LLM, and shows the code inside chunks. It
additionally locations three buttons on the high of every chunk. One to repeat the code to the
clipboard, the opposite to repeat it on to your energetic script in RStudio, and
one to repeat the code to a brand new script. To shut the app, press the ‘Escape’ key.

Urgent the ‘Settings’ button will open the defaults that the chat session
is utilizing. These could be modified as you see match. The ‘Immediate’ textual content field is
the extra textual content being despatched to the LLM as a part of your query.

Determine 3: chattr’s UI – Settings web page

Personalised setup

chattr will attempt to establish which fashions you will have setup,
and can embody solely these within the choice menu. For Copilot and OpenAI,
chattr confirms that there’s an out there authentication token as a way to
show them within the menu. For instance, when you’ve got solely have
OpenAI setup, then the immediate will look one thing like this:

chattr::chattr_app() #> ── chattr - Accessible fashions #> Choose the variety of the mannequin you want to use: #> #> 2: OpenAI - Chat Completions - gpt-3.5-turbo (gpt35) #> #> 3: OpenAI - Chat Completions - gpt-4 (gpt4) #> #> Choice: >
library(chattr) chattr_use("gpt4") chattr_app()

You may as well choose a mannequin by setting the CHATTR_USE surroundings
variable.

Superior customization

It’s attainable to customise many facets of your interplay with the LLM. To do
this, use the chattr_defaults() operate. This operate shows and units the
extra immediate despatched to the LLM, the mannequin for use, determines if the
historical past of the chat is to be despatched to the LLM, and mannequin particular arguments.

For instance, it’s possible you’ll want to change the utmost variety of tokens used per response,
for OpenAI you should utilize this:

# Default for max_tokens is 1,000 library(chattr) chattr_use("gpt4") chattr_defaults(model_arguments = checklist("max_tokens" = 100)) #> #> ── chattr ────────────────────────────────────────────────────────────────────── #> #> ── Defaults for: Default ── #> #> ── Immediate: #> • {{readLines(system.file('immediate/base.txt', bundle = 'chattr'))}} #> #> ── Mannequin #> • Supplier: OpenAI - Chat Completions #> • Path/URL: https://api.openai.com/v1/chat/completions #> • Mannequin: gpt-4 #> • Label: GPT 4 (OpenAI) #> #> ── Mannequin Arguments: #> • max_tokens: 100 #> • temperature: 0.01 #> • stream: TRUE #> #> ── Context: #> Max Information Recordsdata: 0 #> Max Information Frames: 0 #> Chat Historical past #> Doc contents
Modify immediate enhancements

Past the app

Along with the Shiny app, chattr provides a few different methods to work together
with the LLM:

Use the chattr() operate

Spotlight a query in your script, and use it as your immediate

> chattr("how do I take away the legend from a ggplot?") #> You possibly can take away the legend from a ggplot by including #> `theme(legend.place = "none")` to your ggplot code.
right here.

RStudio Add-ins

chattr comes with two RStudio add-ins:

Determine 4: chattr add-ins

You possibly can bind these add-in calls to keyboard shortcuts, making it simple to open the app with out having to jot down
the command each time. To discover ways to try this, see the Keyboard Shortcut part within the
chattr official web site.

Works with native LLMs

Open-source, educated fashions, which can be capable of run in your laptop computer are extensively
out there immediately. As a substitute of integrating with every mannequin individually, chattr
works with LlamaGPTJ-chat. This can be a light-weight software that communicates
with a wide range of native fashions. Presently, LlamaGPTJ-chat integrates with the
following households of fashions:

GPT-J (ggml and gpt4all fashions)

LLaMA (ggml Vicuna fashions from Meta)

Mosaic Pretrained Transformers (MPT)

LlamaGPTJ-chat works proper off the terminal. chattr integrates with the
software by beginning an ‘hidden’ terminal session. There it initializes the
chosen mannequin, and makes it out there to begin chatting with it.

To get began, it is advisable to set up LlamaGPTJ-chat, and obtain a suitable
mannequin. Extra detailed directions are discovered
right here.

chattr seems to be for the situation of the LlamaGPTJ-chat, and the put in mannequin
in a particular folder location in your machine. In case your set up paths do
not match the places anticipated by chattr, then the LlamaGPT won’t present
up within the menu. However that’s OK, you may nonetheless entry it with chattr_use():

library(chattr) chattr_use( "llamagpt", path = "[path to compiled program]", mannequin = "[path to model]" ) #> #> ── chattr #> • Supplier: LlamaGPT #> • Path/URL: [path to compiled program] #> • Mannequin: [path to model] #> • Label: GPT4ALL 1.3 (LlamaGPT)

Extending chattr

chattr goals to make it simple for brand new LLM APIs to be added. chattr
has two elements, the user-interface (Shiny app and
chattr() operate), and the included back-ends (GPT, Copilot, LLamaGPT).
New back-ends don’t should be added instantly in chattr.
In case you are a bundle
developer and want to reap the benefits of the chattr UI, all it is advisable to do is outline ch_submit() technique in your bundle.

The 2 output necessities for ch_submit() are:

As the ultimate return worth, ship the complete response from the mannequin you might be
integrating into chattr.

If streaming (stream is TRUE), output the present output as it’s occurring.
Usually by means of a cat() operate name.

Right here is an easy toy instance that exhibits how one can create a customized technique for
chattr:

library(chattr) ch_submit.ch_my_llm <- operate(defaults, immediate = NULL, stream = NULL, prompt_build = TRUE, preview = FALSE, ...) { # Use `prompt_build` to prepend the immediate if(prompt_build) immediate <- paste0("Use the tidyversen", immediate) # If `preview` is true, return the ensuing immediate again if(preview) return(immediate) llm_response <- paste0("You stated this: n", immediate) if(stream) { cat(">> Streaming:n") for(i in seq_len(nchar(llm_response))) { # If `stream` is true, ensure that to `cat()` the present output cat(substr(llm_response, i, i)) Sys.sleep(0.1) } } # Make sure that to return your entire output from the LLM on the finish llm_response } chattr_defaults("console", supplier = "my llm") #> chattr("good day") #> >> Streaming: #> You stated this: #> Use the tidyverse #> good day chattr("I can use it proper from RStudio", prompt_build = FALSE) #> >> Streaming: #> You stated this: #> I can use it proper from RStudio
right here.

Suggestions welcome

After making an attempt it out, be happy to submit your ideas or points within the
chattr’s GitHub repository.

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Posts additionally out there at r-bloggers

Why Are Extra Folks Utilizing This Purchaser’s Information?

Robotics

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20 August 2024
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Within the aftermath of the pandemic, the enterprise setting has pushed producers to guage (or reevaluate) automation. Guide, repetitive duties – resembling pallet stacking – are thought of a logical preliminary focus.

Nevertheless, suggestions from people looking for options stays constant: the fragmented palletizing automation market makes navigating data difficult and time-consuming. That is why extra companies are utilizing the Automated Palletizing Purchaser’s Information.

Here’s what we all know:

Time is finite – Technical specialists have restricted bandwidth for exploring enchancment choices, whereas high executives can not afford prolonged or repetitive proposals.

Uptime is essential – Whereas manufacturing uptime has at all times been paramount, at this time’s strain is particularly acute. Stopping downtime is a precedence.

Managing margins is difficult – Between half, labor, and transportation prices, the battle to take care of the underside line is getting harder. In consequence, manufacturing should improve their each day, weekly, and month-to-month shipments.

Buyer developments change rapidly – Companies should adapt to evolving shopper preferences and developments at a second’s discover.

Robotiq noticed an issue that wanted an answer. That is why we created the Automated Palletizing Consumers Information. This compiles our business experience right into a sensible information that assists customers in researching, deciding on, and presenting an automatic palletizer resolution. This weblog article focuses on step one of the method: the Strategic Crucial Framework.

Introducing the Strategic Crucial Framework

This Consumers Information is a top-down strategy to deciding on the correct palletizing resolution for any enterprise. Step one is the Strategic Crucial Framework, a high-level define of the enterprise challenges and alternatives.

The Strategic Crucial Framework stems from the Lean Robotics methodology, whose function is to: Empower Folks, increase Productiveness, and foster Adaptability on the manufacturing facility ground and maximize automation influence by environment friendly deployment phased in Design, Combine, and Function.

Influence Class: The What

The Strategic Crucial Framework begins with the influence classes of Folks, Productiveness, and Adaptability. Every of those strains is ranked in line with its stage of significance. The desk beneath summarizes every influence class situated on the left facet of the downloadable Strategic Crucial Framework.

Class

Particular Component

Component Particulars

Folks

Well being & Security

Improve palletizing security and cut back well being dangers.

Labor Availability

Cut back dependency on handbook labor for hard-to-fill roles.

Worker Engagement

Improve worker progress and retention.

Productiveness

Effectivity & Throughput

Improve throughput and stop bottlenecks.

Consistency

Improve pallet integrity and consistency.

Adaptability

Continuity

Cut back downtime and cut back dependency on a couple of operators.

Flexibility

Meet cycle-time wants of the worth stream in actual time; cut back changeover time and complexity.

Scalability

Handle fluctuating calls for, react to progress, and meet ROI.

Resolution Class: The How

The score of resolution mechanics is on the correct facet of the Strategic Crucial Framework. It will problem the staff to force-rank crucial classes. It will present focus for the staff when it transitions into researching the palletizing resolution classes.

Class

Particular Component

Component Particulars

Design

Pre-Engineered Precision

Cut back design prices, stop scope creep, and cut back dangers related to custom-engineered designs.

Maximized Efficiency

Maximize payload and decide fee. Lower footprint.

Turnkey Resolution

Cut back choice and set up complexity.

Combine

Quick Set up

Cut back system set up and coaching time.

Manufacturing unit Match

Decrease influence to manufacturing facility format; reduce rearranging upstream and downstream operations.

Function

Reliability

Improve manufacturing uptime and cut back unplanned downtime.

Management

Preserve management of product dealing with, improve software program usability, and modify applications autonomously.

The Strategic Crucial Framework

The Strategic Crucial Framework is a downloadable software that’s designed for use and saved by the analysis staff. Whereas not supposed presentation materials, this framework is a vital baseline for the remainder of the method.

Subsequent Steps

As soon as accomplished and memorized, the Strategic Crucial Framework shall be put apart (quickly). The following step within the course of is to know the completely different resolution classes accessible in the marketplace. The enterprise precedence and resolution panorama will then come collectively in later chapters.

The Automated Purchaser’s Information walks groups by the next steps:

1. Strategic imperatives:

Begin by revisiting the Strategic Imperatives Framework from the start of the method. These are the guiding ideas to your decision-making course of. Following this, the Enterprise Alignment Matrix ought to assist align these enterprise ideas to the completely different palletizing resolution classes.

2. Element specs:

Summarize the technical specs and spotlight particular options (and features) which are important with the Palletizing Course of Detailed Specs desk.

3. Resolution scores:

Assessment the earlier instruments to research the palletizing choices. Categorize every primarily based on how effectively they align with the strategic imperatives and specs.

4. Value evaluation:

Present an outline of the fee evaluation, detailing the preliminary investments, working prices, and scalability implications for every resolution.

5. Choice-making framework:

Switch required data to your online business’s decision-making framework.

6. Conclusion and advice:

Based mostly on the alignment along with your strategic imperatives, specs, and value evaluation, current the answer that emerged as probably the most appropriate.

7. Pitch and justification:

Utilizing the data documented within the Automated Palletizing Purchaser’s Information, develop a persuasive argument to your chosen resolution. Spotlight its advantages, distinctive worth proposition, and the way it aligns along with your firm’s long-term targets.

8. Inner communication:

Put together a transparent and concise presentation (or doc) to share with related stakeholders in your group. Make sure that your justification is well-structured and data-driven.

The Automated Palletizing Purchaser’s Information is out there, together with all of the instruments, from Robotiq.

Transferring to Motion

The post-pandemic enterprise panorama has prompted producers to reassess the function of automation, significantly in addressing handbook, repetitive duties like palletizing. Nevertheless, suggestions signifies that navigating the fragmented palletizing automation market stays a major problem. That is why we created the Automated Palletizing Purchaser’s Information.

The Automated Palletizing Purchaser’s Information compiles business experience right into a sensible useful resource for researching, deciding on, and presenting automated palletizer options. This information introduces the Strategic Crucial Framework, a high-level strategy to aligning enterprise challenges and alternatives with automation options. By leveraging this framework, companies could make knowledgeable selections that drive productiveness, empower staff, and improve adaptability on the manufacturing facility ground. By environment friendly deployment and phased integration, companies can maximize the influence of automation and deal with their distinctive operational wants.

Extra Assets

Are you aware of the idea of LEAN ROBOTICS? With their intensive expertise aiding tens of 1000’s of producers on their automation journey, the Robotiq staff supplies perception on tips on how to keep centered through the course of. Our LEAN ROBOTICS e-book is out there in textual content or as an audiobook. Click on right here for extra data.

Prepared to begin? Speak with certainly one of our palletizing consultants.

Superior System for Microbiome Sampling – NanoApps Medical – Official web site

Nanotechnology

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20 August 2024
0

Steps are being taken towards human scientific trials for the precision measurement of the intestinal microbiome.

Researchers at Tufts College College of Engineering have made exceptional strides in creating a small, pill-sized gadget that may be swallowed to traverse the gastrointestinal tract, offering a complete sampling of the microorganisms current inside it.

This gadget has the potential to advance analysis on the connection between resident micro organism and a variety of well being circumstances. It might additionally function a diagnostic software for adjusting the microbiome or administering medicine to deal with these circumstances.

The gadget has accomplished preclinical characterization – as introduced within the journal System – paving the best way for upcoming human scientific trials. It’s characterised by a 3D-printed tender elastic exterior with sidewall inlets that open in response to the altering acidity because the tablet reaches the small gut. The tablet makes use of elastic microvalves with swellable polyacrylate beads that shut the inlets as soon as intestinal content material has been collected.

The expertise was developed at Tufts Nano Lab by a workforce led by Professor Sameer Sonkusale, together with post-doctoral researcher Ruben Del-Rio-Ruiz who’s lead creator on the preclinical examine. A second workforce of researchers led by Professor Giovanni Widmer on the Cummings College of Veterinary Drugs at Tufts College assisted by PhD candidate Debora Silva, carried out the exams in animals and analyzed the samples collected by the tablet.

Technological Enhancements and Advantages

Enhancements over earlier variations of the tablet embrace utilizing a tender elastic exterior reasonably than a inflexible shell, to make it simpler to ingest, and considerably improved management over localized sampling of the microbiome within the small gut.

Present methods to check the intestinal microbiome primarily depend on fecal matter, This expertise represents a major advance in understanding the operate of the hundreds of microbial species populating the complete size of the gastrointestinal tract and their results on well being.

Reference: “Smooth autonomous ingestible gadget for sampling the small-intestinal microbiome” by Ruben Del-Rio-Ruiz, Debora Regina Romualdo da Silva, Hasika Suresh, Hannah Creasey, Cihan Asci, Danilo M. dos Santos, Atul Sharma, Giovanni Widmer and Sameer Sonkusale, 20 June 2024, System.
DOI: 10.1016/j.gadget.2024.100406

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