Police say an AutoZone in South Los Angeles was looted again by a large crowd after a street takeover at Century Boulevard and Hoover Street.

AutoZone store looted again following street takeover in South Los Angeles

If you think AutoZone's Fix Finder service might be of use to you, first familiarize yourself with what it is and the issues it will look for.

What Is AutoZone's Fix Finder Service And What Issues Does It Look For?

The Texas Tech Red Raiders (8-4) take on the Arkansas Razorbacks (6-6) in the AutoZone Liberty Bowl on Friday

How to Watch Texas Tech vs Arkansas, Live Stream Liberty Bowl, TV Channel

Carter Worth breaks down the trade in AutoZone.

The market is giving an opportunity to buy one of the best performing stocks ever, says Carter Worth

A fire in an AutoZone automotive parts store in Hollywood burned through neighboring businesses before being knocked down Saturday night.

Fire at AutoZone store in Hollywood burns through neighboring businesses, forces street closure

TD Cowen analyst Max Rakhlenko maintained a Buy rating on AutoZone (AZO – Research Report) today and set a price target of $3,450.00. Max ...

Buy Rating Affirmed for AutoZone Amidst DIFM Growth and Strategic Expansions Despite Modest FY25 EPS Outlook

Looters stole $67,000 in merchandise during a flash-mob burglary at an AutoZone store in South LA in June, the LAPD said. New photos were released.

Looters stole $67,000 in merchandise during flash-mob burglary at AutoZone in South LA, police say

A bipartisan group of lawmakers has questioned major auto parts retailers about their procurement from Qingdao Sunsong, amid concerns of tariff evasion via transshipping through Thailand. Letters to CEOs of AutoZone, O’Reilly Auto Parts, and others demand compliance with U.S. trade laws. The issue involves significant tariffs imposed in 2019 on Chinese auto parts.

Lawmakers Scrutinize Auto Parts Retailers Over Tariff Evasion Allegations

A bipartisan group of U.S. lawmakers has questioned major auto parts retailers regarding their purchase of products potentially evading U.S. tariffs from a Chinese company, Qingdao Sunsong. Key figures involved include Representative John Moolenaar, Democrat Raja Krishnamoorthi, and Senators Sherrod Brown and Bill Cassidy. Retailers under scrutiny include AutoZone, O’Reilly Auto Parts, and others.

Lawmakers Press Auto Parts Retailers Over Alleged Chinese Tariff Evasion

Analysts have been eager to weigh in on the Consumer Cyclical sector with new ratings on Tractor Supply (TSCO – Research Report), AutoZone (AZO...

Analysts Offer Insights on Consumer Cyclical Companies: Tractor Supply (TSCO), AutoZone (AZO) and Dollar General (DG)

AutoZone

autozone

I'm sure you've heard; DevOps is all the rage n insert fancy clothes and glass of champag e. No, but really. It's pretty popular right now. Interestingly enough, many organizations are hiring for it and have no idea what its purpose is. Some feel that it's an infrastructure role, others feel it's to look at CICD pipelines all day. But what does it truly mean? What's DevOps? Originally, DevOps was created and coined in 2009 by Patrick Debois. The purpose that he wanted to obtain with DevOps was to break down barriers between development and operations teams. If you've been in tech long enough, you know there used to be (and still is in some places) a mindset of: Give the code to the operations people so they can deploy it The code breaks the server The operations people don't want to deploy any changes because they want system uptime The developers want the code change because it's a new feature, hotfix, or component of an application that needs to be deployed Everyone is fighting There was a lot of back and forth. Both teams blamed each other and nothing got done. DevOps, originally, was supposed to stop that from happening. DevOps is the combination of bringing teams together and making workloads more efficient for everyone. It's Not A Title When you ask someone if DevOps is supposed to be a title, most engineers will tell you no. I'm in the category that it shouldn't be and instead, it should be a practice. However, that battle has been more or less lost. If you're still in the camp that it shouldn't be a title, then you're not wrong. DevOps, by definition, is a practice that should be within all engineering teams. It wasn't meant to be a specific team itself. It was meant to break down silos, not create a new one. It Is A Title Nowadays, you see titles all over the place. It's definitely an annoyance for folks that wanted DevOps to be what it was supposed to be, but the truth of the matter is times change. In a few years, the title will go away for something else. That's just how it goes. DevOps Engineer DevOps Engineer The thing to remember is this; the DevOps Engineer title is one of the highest-paying tech jobs in today's world, so I'd like for you to think about it like this: If it's the type of work you want to be doing and it pays what you want, who cares? Have fun and enjoy. Why It's Important Let's move away from the notion of what DevOps is supposed to be, and instead, think about what it is today. It's literally a job that does everything; development, operations, CICD, containers, orchestration, scalability, cloud... and the list doesn't stop there. The DevOps Engineer job today is one that is not a beginner-level role. It's very much an advanced, level 400 role. It's extremely important to keep that in mind. With that being said, the question becomes - why is it so important? In today's world, every company that isn't a software company is a software company. Let's take an auto-parts store like Auto Zone as an example. They're a car company, right? Well, here's the thing; they have an app, and a lot of people use that app. It's used to buy car parts, look car parts up, hold membership information, etc... That's a key part of the business. Because of that, they're now thinking more about software. Every organization, regardless of whether they're a tech company or not, is now always thinking about software. When thinking about software, management typically thinks about: How can we get features and the product into the hands of the customers faster How can the application and systems not go down How can we move faster When you think about it, that all falls under DevOps teams. The DevOps teams are pretty much the people that are running the company at this point. DevOps vs SRE I get asked this question a lot; Regardless of who you ask, you'll always get a different answer. What a lot of engineers do, including myself, is to follow the Google SRE handbook. If you haven't read the handbook, you can get it for free . When it comes to SRE and DevOps, DevOps is a subset of Site Reliability Engineering. I like to think of it as a child object of SRE. Instead of DevOps being a title, the practices of DevOps are embedded into the SRE role. what's the difference between DevOps and SRE? here Wrapping Up This blog post is the first of many in the DevOps/SRE Series. The goal of this series is to help engineers from all over the world break into DevOps and understand what's happening in the DevOps space. I believe you'll get a ton of value out of this and enjoy the process along the way. Thank you so much for reading! First published here

Should Anyone be Called a DevOps Engineer?

During “Hacker Week” in Las Vegas, thousands of information security professionals that include actual hackers, converge. It is best to be alert since there are bad actors who have rogue intentions. Finding victims can be easy when there is no awareness of the imminent threats. With that said, car hacking has become more commonplace in recent years, due to the increased integration with electronic systems that include the car’s own lock system. With keyless entry systems, it uses wireless or radio signals to unlock the car. These signals can in turn be intercepted and used to break into the car and even start it. One such technique is called SARA or Signal Amplification Relay Attack. All roads lead to DEF CON 2018 (Las Vegas, NV) This keyless entry hack is probably one of the simplest out there. This follows a similar principle to “relays” which transmit an RFID signal across a longer distance. Keyless entry systems use a “handshake” signal that sends a signal from the key fob to the car’s lock controller unit. When the fob is within proximity of the car, the controller unit detects it and unlocks the car automatically. The radio transmitter relay device works on that principle using two devices. One device must be placed next to the car, while the other must be within close proximity to the car owner’s key fob. A signal is boosted from the car to the fob to trick it in a sense by sending the signal from the device next to the car to the device that is near the key fob. Then the signal is copied rather than decrypted and can be used to not only open the car, but to start the car as well. So it doesn’t even try to decrypt the signal, just copy and use. It’s like intercepting someones private key. There are vulnerable car models, mostly European. There is no fix or update to download to fix the problem unfortunately. Covering your key fob with foil or a faraday cage certified box works, but to the owner’s inconvenience. You can ask your car dealer if the car model you have is vulnerable to this hack. They may or may not know, so consulting the auto manufacturer might be best. Old school solution I would suggest is to get a anti-theft lock on the car’s steering wheel or brake pedal. Those are not electronic and use actual keys. Other solutions include putting the key fob in a shielded pouch or a signal blocking fob holder. Anti-theft steering wheel lock, The Club, is one of the best ways to secure against SARA (Source: Autozone) In 2016, security researchers at the Munich-based automobile club ADAC their findings of attacks they performed to test the vulnerability. They did this to 24 vehicles from 19 different manufacturers. published Here is a list of some cars affected: https://www.forbes.com/sites/thomasbrewster/2016/03/21/audi-bmw-ford-thief-car-hacking/#2cccd9514f1e This information is given for educational and reference purposes only. Hacking for malicious purposes is an illegal activity which is punishable by the law. Note:

Signal Amplification Relay Attack (SARA)

Recently, we discussed how developers could make use of the . In that post, we shared a simple Node.js script that would call one of the APIs using a file stored in Azure. Photoshop API It was simple — get an access token, start an API job with an input document, poll for completion, and then check the result. Simple is good though as it means it’s easy to integrate into custom workflows. In today’s post, we’ll do just that. Our Workflow Let’s begin by defining what our workflow is going to do. Imagine we are using a cloud storage provider, in this case, Dropbox, to store photos. Our workflow is going to monitor this folder and trigger when new pictures are added. When the picture is added, we will grab the file and send it to an endpoint that runs the Lightroom feature. This feature attempts to correct exposure, contrast, and other issues in photos. “AutoTone” As an example (that we stole right from the ), here’s a before and after showing the impact of the corrections: docs Once the API is done, we will then store the result in folder in Dropbox. We could overwrite the original, but most likely, people will want both copies so they can make a final check to see which they prefer. another The Worfklow Platform For our workflow, we will use . Pipedream is a low-code/no-code solution for building generic workflows. It’s in the same space as Microsoft PowerAutomate and Workfront Fusion. (And you can expect to see examples from us soon on those platforms as well.) Pipedream Pipedream really helps developers by making the more difficult, or even boring, aspects of a project “plug and play.” As an example, when defining what starts a workflow, a trigger, Pipedream comes with a huge set of pre-built code blocks. When we start demonstrating how we built our workflow, you’ll see that in action. Pipedream has a free tier, so those of you reading who want to follow along can do so after signing up. If you’ve never used Pipedream before, check out their excellent for an introduction to how it works. They also have excellent . Finally, your friendly author here has about Pipedream as well. Pipedream University docs written With that out of the way, let’s get building! Step One — The Trigger Pipedream workflows start with a trigger that represents the “event” that should kick off our process. Pipedream has many, of these triggers built-in (and you can write your own), and luckily, a “New File” in Dropbox trigger is supported: many After selecting the “New File” trigger, you are given a simple interface to configure it: On top is the authentication aspect. Here, you can connect Pipedream to your Dropbox account. Best of all, once you’ve done that one time, you can reuse that connection in multiple workflows. Next, make a note of the Path value. You can type in here, and Pipedream will attempt to autocomplete based on your account, or simply enter a path. We’re going to use as the folder that expects to get images. /PSAPI_Input The next two settings can be ignored, but the final setting, , should be toggled to true. We're going to need that link so we can tell the API how to fetch the data. Include Link Here’s the final configured trigger for our workflow: To recap, at this point, we’ve configured the workflow to fire automatically as soon as a file is added to a particular folder in our Dropbox account. Step Two — Get an Upload URL When the Photoshop API runs, it expects at minimum two things — an input URL and an output URL. Basically, where to read its input and where to store the result. Our input is going to be the new file added to Dropbox. Our output is going to be the location of the image corrected by Lightroom. To support this, we need to ask Dropbox to generate a special URL that can be used to store data. Pipedream has a lot of Dropbox actions built-in but unfortunately, does not have this particular one. Fortunately, Pipedream does let you create a step with custom code where it’s already handled the authentication for you. Remember that in the trigger, we specified an existing Dropbox account. Because we did this, we can then write code and let Pipedream handle authentication. By consulting the , we find the endpoint which is exactly what we need. Dropbox API get_temporary_upload_link In Pipedream, we add a new step and select “Use any Dropbox API in Node.js.” This gives us boilerplate code hitting one sample endpoint: https://gist.github.com/cfjedimaster/867a554f8f046ac357c89ae156441aae?embedable=true#file-script-js Most importantly — note the authentication information is automatically provided. All we need to do then is to edit the endpoint and set our input: https://gist.github.com/cfjedimaster/cf4932ba1d66887a7a75e3a5e9169d0e?embedable=true#file-script-js Note we are using a path that’s a different folder, . The value after that, , demonstrates using information from earlier in our workflow, specifically the filename of the event that triggered our workflow, and the name of the file itself. /PSAPI_Output ${steps.trigger.event.name} The code ends by returning the result of the API call, which in our case, is going to be a special URL we can use with our API call. Step Three — Getting an Access Token In our , we discussed how to get credentials for the Photoshop API and how then to use them in code to get an access token. In Pipedream, we can add a step to our workflow to run custom code. previous blog post You saw that in the previous step when we hit the Dropbox API. Pipedream supports both Node.js and Python so you’ve got a few options there. We will add a new code step and name it . getAccessToken This step will be responsible for using our credentials to generate a JWT and exchange that for an access token. In that previous blog post, the code made use of the package to simplify the process. However, this package does not work as an ES6 import which Pipedream supports. Luckily, we can swap out to another NPM package, . Let’s take a look at the code: @adobe/jwt-auth jsonwebtoken https://gist.github.com/cfjedimaster/54fbfc2c4a8e90ffda5f6007ddb13714?embedable=true#file-script-js So a couple of things here. First, every Node Pipedream code step uses a form like so: https://gist.github.com/cfjedimaster/a12dd06e92a801ae7572616709f04303?embedable=true#file-script-js The function is called automatically, and passed data from any previous steps as well as a handler ( ) for other operations we won't need. Basically, we will put our imports on top and our logic inside. run $ You can see a set of variables being copied from the environment as — not surprisingly — Pipedream lets us define custom environment variables as well. The next block of code generates our JWT. This is mostly boilerplate, but pay special attention to the part. This variable, , is what sets the scope for our token and is required to work with the APIs. jwtOptions "https://ims-na1.adobelogin.com/s/ent_ccas_sdk": true, After the JWT is created, it can be sent to a generic Adobe endpoint to generate an access token. The final bit of logic is to return that token. If you remember in the previous step, anything we return can be used later. You’ll see this in action soon. Step Four — Calling the Lightroom Autotone API Now, it’s time to get down to business. Our trigger gave us a link to the new image. The next code step generated a link for us to upload the final result. We then got an access token. We’ve got everything needed to start the process. Once again, we’ll add a Node.js step to our workflow. Here it is in action. https://gist.github.com/cfjedimaster/605a25a51602bd91eaeafb0bbd68d02a?embedable=true#file-script-js The requires a few parameters, in this case, an input and output value. In our case, we pass in the link from the trigger and the special upload URL generated earlier. Autotone API And that’s it! The result of this call is a link to the job which we return at the end. Step Five — Do Nothing Ok, not exactly , but here’s an interesting question. This entire workflow runs automatically with no human interaction. The previous step kicks off a process that, when done, will save the result to Dropbox. Do we need to do anything else? Not really. nothing It’s absolutely possible the API may fail for some reason. It’s possible something else could go wrong as well. It’s also possible we may want to alert someone about the change, perhaps via email. Honestly, it’s up to you. In our sample workflow, we decided to simply check the job and wait for it to finish. Here’s that code step, and it’s pretty similar to our previous blog post. We check the job, wait, and check it again. https://gist.github.com/cfjedimaster/207617f3a824c5cb6ef22f43983a9a1b?embedable=true#file-script-js This is where we end the workflow, but we could, and probably should, add logic to check the result of the job and do something. Perhaps on a good result, we do nothing, but on error, we then send an email. (And by the way, Pipedream makes sending emails ludicrously easy.) What’s nice though is that we can decide that later. One really nice feature of Pipedream is that it makes it easy to check when workflows have run. For example, here’s a list of past executions: The errors you see there came from me playing with the API and figuring out how it worked. You can also click on any of them and see the data that flowed throughout it. The Result With the workflow in place, we uploaded a new image to a Dropbox folder, watched the workflow firing in an open tab, and when done, checked the result. Here’s before: And here’s after: The result is crisper, and absolutely an improvement! You can create your own copy of my workflow here: . If you want to learn more, visit our , and share what you’ve built! https://pipedream.com/new?h=tch_3xxfJA docs Also published here

How to Automate Image Workflows With the Photoshop API

ফটোশপ এপিআই দিয়ে কীভাবে ইমেজ ওয়ার্কফ্লো স্বয়ংক্রিয় করবেন

So automatisieren Sie Bild-Workflows mit der Photoshop-API

Cómo automatizar flujos de trabajo de imágenes con la API de Photoshop

Comment automatiser les flux de production d'images avec l'API Photoshop

फ़ोटोशॉप एपीआई के साथ छवि वर्कफ़्लो को स्वचालित कैसे करें

Photoshop API を使用して画像ワークフローを自動化する方法

Photoshop API를 사용하여 이미지 작업 흐름을 자동화하는 방법

Como automatizar fluxos de trabalho de imagem com a API do Photoshop

Photoshop API ile Görüntü İş Akışlarını Otomatikleştirme

Cách tự động hóa quy trình làm việc với hình ảnh bằng API Photoshop

如何使用 Photoshop API 自动化图像工作流程

GraphQL is an API technology that is rapidly gaining popularity among thousands of enterprises around the world. It was built to improve many aspects of REST, one of them being that GraphQL provides a native way to expose an API schema. This schema makes it easier than ever to generate API documentation with little effort. Introducing Magidoc Magidoc is an open source tool that can be used to easily autogenerate static GraphQL documentation for any GraphQL API, either from SDL files or from a live endpoint using the introspection query. Not only can you use Magidoc to generate beautiful and easy to understand API documentation out of the box, but you can also provide custom pages using markdown. These pages allow you to present your API however you want. For instance, you could describe different concepts, examples, flows, URLs, etc. Getting started Schema (SDL) First, we need a schema. For the sake of this demonstration, let’s use a simple TODO application schema. In a file named , we will put the following code. schema.graphqls type Todo {
  id: ID
  name: String
  complete: Boolean
}

input TodoInput {
  todoId: ID
  name: String
  complete: Boolean
}

type Query {
  """
  Returns all TODOs 
  """
  todos: [Todo]
  """
  Return a specific TODO by ID. 
  """
  todo(todoId: ID): Todo
}

type Mutation {
  """
  Creates a TODO and returns it.
  """
  createTodo(input: TodoInput!): Todo
  """
  Updates a TODO or returns an error if it does not exist.
  """
  updateTodo(input: TodoInput!): Todo
  """
  Toggles a TODO or returns an error if it does not exist.
  """
  toggleTodo(todoId: ID!): Todo
  """
  Deleted a TODO or returns an error if it does not exist.
  """
  deleteTodo(input: TodoInput!): [Todo]
} Magidoc configuration Then, we need a Magidoc configuration file. Magidoc uses configuration as code, which means that the configuration is nothing more than a Javascript file. This allows you to implement logic in it. For our example, let’s write the following configuration in a file called : magidoc.mjs export default {
  introspection: {
    type: 'sdl',
    paths: ['./schema.graphqls'],
  },
  website: {
    template: 'carbon-multi-page',
    options: {
      appTitle: 'Medium Article',
      appLogo: 'https://seeklogo.com/images/P/Pokemon-logo-497D61B223-seeklogo.com.png',
      pages: [{
        title: 'Medium Article',
        content: `
# Medium Article
Congratulations! You've successfully completed the Magidoc tutorial.
## Where to go next?
- Star the project on [GitHub](https://github.com/magidoc-org/magidoc) 
- Read the [documentation](https://magidoc.js.org/introduction/welcome)
`
      }],
    },
  },
} In this example, we load our schema from an SDL file. We also configure different website options, like the , and a custom page that contains markdown that will be rendered by Magidoc in the output website. appTitle appLogo Magidoc supports advanced markdown features like tables, ordered and unordered lists, inline HTML and much more. Build it Finally, to build our website, we’ll execute the following commands: npm install --global @magidoc/cli@latest
magidoc generate
magidoc preview These commands will install Magidoc, generate the website and then preview the output locally. And there you have it! A static GraphQL API documentation. Conclusion Magidoc is a powerful tool that can help you build superior GraphQL documentation very easily. This tutorial only scratches the surface of all that is possible to do with Magidoc. Many more advanced features exist, like a development server with hot-reload, the ability to provide custom assets (images or videos), full customization of the template using Svelte & Svelte-Kit, and many more built-in customization. Make sure to check out the documentation to learn how you can turn your GraphQL schema into branded documentation website for your company, and have a look at the if you’re interested in contributing. Github repository Thank you for reading!

Autogenerate Your GraphQL Documentation With Magidoc

TLDR (30s) To quickly get your own customizable Webhook URL, simply this . You can change the name of the project if you'd like, as it's reflected in the webhook URL that's generated. Fork Source in Autocode Once you've created the Fork you'll be brought into the Autocode editor. Click on the blue Deploy button at the bottom left corner of the editor. Once it's deployed, open the file to view the live Webhook URL at the bottom of the editor. functions/__main__.js That's it! You can add whatever custom handling logic you'd like to the webhook at anytime, and simply click deploy again! : Make sure you include the at the end of the url otherwise it will be redirected and the payload will be dropped. Note / --url https://YOUR_USERNAME.api.stdlib.com/my-webhook@dev/ \
    --header 'content-type: application/json' \
    --data '{
  "some_data": "This is sample data",
  "more_data": "More sample data"
}' --url 'https://YOUR_USERNAME.api.stdlib.com/my-webhook@dev/?some_data=%22This%20is%20sample%20data%22&more_data=%22More%20sample%20data%22' $ curl --request POST \ # OR $ curl --request GET \ Introduction Often when working on projects that involve integrating different tools together or syncing data between different services, you may find yourself utilizing . A lot of tools/services allow you to set a webhook URL that they can then push data to based on some triggers. The webhook server behind that URL can then perform custom logic with that data. webhooks Sounds pretty straightforward right? However, you'll realize you have to provision a new server, develop and deploy a web app, and worry about other administrative tasks such as maintenance and scalability. Not to mention, every time you want to make changes or test different payloads, you'd need to go through the deployment process again. This adds a lot of hurdles just to perform a simple task of receiving and handling data from an external service. We get a lot of questions about setting up webhooks at , and I think it's the easiest way to get started and have a live webhook URL in (literally) seconds. I built a that you can simply fork and deploy to get your own webhook URL in no time! Autocode Source in Autocode Your webhook is deployed on top of serverless technology. That means it will scale for you without any need for administrative efforts on your end. How It Works When your webhook is deployed on Autocode, the endpoint listens to incoming requests. It accepts both and requests. The parameters passed in from either the request body or querystrings are parsed out and included in the object. is a magic parameter that we populate automatically. GET POST context.params context It must be the last parameter in the list of parameters you define in your function signature. You can access the headers or the actual request body and other useful data from within the object: context .exports = (context) => { result = {}; .log( , context.params); .log( , context.http.headers); .log( , context.http.body); result;
}; /**
* An HTTP endpoint that acts as a webhook for HTTP(S) request event
* @returns {object} result Your return value
*/ module async let console 'params:' console 'headers:' console 'body:' return You can also pass in named parameters with type checking included out of the box based on the specification. FunctionScript Simply include the named parameter as an argument in the function and update the comment above the function defining the name and type of that parameter it should expect: .exports = (name = , age, context) => { result = {}; .log( , context.params); .log( , context.http.headers); .log( , context.http.body); result;
}; /**
* An HTTP endpoint that acts as a webhook for HTTP(S) request event
* @param {string} name
* @param {number} age 
* @returns {object} result Your return value
*/ module async "Bob" let console 'params:' console 'headers:' console 'body:' return Testing with Payloads You can test your webhook URL before deploying it from within the Autocode editor. Press on the at the top of the endpoint file: Edit Test Event Payload That will open up the payload editor where you can enter a JSON formatted payload for testing: Once you've finished setting a test payload and saved it, click on the green button at the bottom right corner of the editor. Run Test Event That will then open up a console displaying any logs or errors you may have: Deploying your Webhook When you're ready to deploy your webhook URL and start listening to incoming events, all you need to do is press on the blue button at the bottom left corner of the editor: Deploy Once it's deployed, the URL displayed is now ready to handle events from any external service. You can also view real time logs by clicking on the View Logs button in the same corner of the editor. : Make sure you include the at the end of the URL otherwise it will be redirected and the payload will be dropped. Note / That's It! Thanks for checking out this post! Hope it helped, if you have any questions please feel free to reach out to me at . I'd love to hear about what you're building! You can also follow the Autocode team on Twitter for more updates . yusuf@autocode.com @AutocodeHQ Yusuf is a software engineer at Autocode . Previously published at https://dev.to/ymusleh/setup-a-custom-webhook-url-in-30-seconds-with-autocode-3ce9

How To Create a Custom Webhook URL Using Autocode

A frame for long-term impact [People] make their own history, but they do not make it just as they please; they do not make it under circumstances chosen by themselves, but under circumstances directly found, given and transmitted from the past. – Karl Marx (quoted by Levi Bryant) Cognitive biologists Maturana and Valera argue that the defining quality of living things is that they are “autopoietic.” literally means self-creation. Living things, Maturana and Valera explain, are able to regenerate their own parts, like how the cells of an animal die and regenerate or how a species reproduces. Autopoiesis Autopoiesis is an intriguing lens through which to understand the aspirations of tech companies. To examine what it means for a tech company to be autopoietic, let’s consider two systems on which it depends: its product and its customers. An autopoietic company maintains a circular loop where the team builds the product, customers use it, and the team is paid to keep iterating. This is otherwise described as product-market fit. Startups are not born autopoietic — they are bootstrapped by investment and the passion of their founders. If these elements dry up before product-market fit is achieved, the team stops building and the company ceases. Startups who achieve autonomous life remain vulnerable like organisms. The departure of a leader, a shift in the technical landscape, or the collapse of a market can lead to a company’s demise. The most ambitious founders want more than a brief period of success: they envision autopoietic institutions that will thrive after their departure, riding waves of technological and social change. To achieve longevity, companies must behave more like species than organisms. Traits for survival must evolve across generations in the midst of environmental flux. Great companies maintain their circular loop of creation as employees are fired and hired, as customers are acquired and lost, and as technology is refactored. They preserve life and identity while their elements radically change. Companies are often advised to define their “DNA.” Genetics is an apt metaphor for the passing of values to new hires. However, genetics is not the only mechanism for passing down survival advantages. In the , philosopher Levi Bryant writes: Democracy of Objects … it’s worth recalling that Darwin nowhere specifies what the mechanism of inheritance is, only that in order for natural selection to take place there must be inheritance. There is thus no reason to suppose that genes alone are the sole mechanism of inheritance. Organisms alter their environments to enhance their future prospects. There is a class of animals known as who change their environment to suit their purposes. Beavers build dams and birds assemble nests. niche constructors For some species, survival is predicated on the non-genetic creations of their predecessors. Bryant quotes developmental systems theorists, Griffiths and Gray: Certain aphid species reliably pass on their endosymbiotic Buchnera bacteria from the maternal symbiont mass to either eggs or developing embryo. The bacteria enable their aphid hosts to utilize what would otherwise be nutritionally unsuitable host plants. Aphids that have been treated with antibiotics to eliminate the bacteria are stunted in growth, reproductively sterile, and die prematurely. Bryant explains: The point here is that the Buchnera bacteria is not a part of the aphid’s genome, but nonetheless plays a significant role in the development of the phenotype. Far from the genes already containing information in the form of a blueprint of what the organism will turn out to be, genes are one developmental causal factor among a variety of others. The notion of non-genetic inheritance suggests that organizations can do more than pass down corporate values to new employees through training and culture. Companies can plant survival advantages in their environment; that is, within systems that exist independently of the team members themselves. Here’s a simple example from my startup. We connected our post-purchase customer survey with our Slack communication system. Whenever a customer submits the survey, the Typeform app posts the response to a Slack channel where everyone sees it. Notifications are sent if the Net Promoter Score is beneath a certain threshold. The result is that a baseline of real-time customer awareness is automatically provided for new employees. New team members don’t need to learn, “we value customer feedback” and then consciously seek it out. Instead, customer feedback is in the environment. Employees “inherit” an advantage out the gate that doesn’t require DNA transfer. already there The circular loop of product creation is bi-directional. Long lasting products must be designed to perturb the team when something needs to be changed. Not only must the team build the product, but the product must build the team. Constructing environmental survival advantages helps companies increase longevity through autopoiesis. However, a system’s achievement of autopoiesis isn’t necessarily good for the world at large. Maturana and Valera argue that the goal of autopoietic systems is to maintain their circular loops of self-creation. Regardless of the mission and intentions of the founders, the most successful companies take on a life of their own, a life who’s overall impact on connected systems is unpredictable. only I’m struck by how startups state their intention to “change the world” or “dent the universe.” Changes and dents, of course, can have both positive and negative impacts. Autopoietic systems created climate change and fake news. Note: Thanks to Jordan Peacock for nudging me down the autopoiesis road and pointing me to Levi Bryant.

Autopoietic Product Thinking

Apache Seata Turns Distributed Transaction Nightmares into Spring Boot Dreams

The AI industry's obsession with Transformers might finally be waning. While OpenAI and Google with their extensive language models capture the public attention, there is a more discreet change taking place in the way AI is implemented into production systems, or how it is AI is utilized within industry processes. Among practitioners who are less concerned with competition metrics and more focused on speed, efficiency, and scalable solutions, newer versions such as Mamba of State Space Models (SSMs) appear to be winning some favor. The theoretical base for the SSM approach has existed for some time, but its practical application in competition against Transformers is relatively recent. The Problematic Yet Powerful Legacy of Transformers The Problematic Yet Powerful Legacy of Transformers Every popular AI service like ChatGPT, GitHub Copilot, or Google Search still heavily rely on Transformers for AI capabilities. They’re flexible, well-documented, and backed by a huge ecosystem of open weights, tutorials, and deployment frameworks. But teams trying to ship production AI know the catch: once your input gets long, the costs start piling up. The self-attention mechanism that gives Transformers their power also makes them scale poorly with input length.
Memory usage can spike unexpectedly.
Latency becomes hard to control. The self-attention mechanism that gives Transformers their power also makes them scale poorly with input length. Memory usage can spike unexpectedly. Latency becomes hard to control. These aren’t theoretical concerns. When a user is stuck staring at a loading spinner while your model processes their 20-page contract, that’s not a “research problem.” It’s a product blocker. SSMs: The Engineering-First Alternative The State Space Models (SSMs) approach sequence modeling differently. Rather than computing attention across the entire input, they utilize state transitions to model dependencies. That gives them a number of advantages: Linear scaling with input length (versus quadratic in Transformers)
Predictable memory usage that stays steady, even as context grows
Faster inference, especially on long-form data
Easier deployment on constrained or edge hardware Linear scaling with input length (versus quadratic in Transformers) Linear scaling Predictable memory usage that stays steady, even as context grows Predictable memory usage Faster inference, especially on long-form data Faster inference Easier deployment on constrained or edge hardware Easier deployment These features were integrated into a model architecture designable within Mamba that was released in late ‘23. For a lot of teams, it just clicked. Even as some of the researchers held on to their skepticism, engineers focused on what really mattered: it was faster, less memory intensive, and more permissive in during demand resource allocation. Real-World Results from Deployment In one recent project, I was tasked with building an AI system that handled freight documents like invoices, bills of lading, and customs entries. These documents were not in the form of simple prompts. They were chaotic, multi-page documents that exceeded 10,000 tokens per file. We built and deployed a Transformer-based pipeline to address the issue. Although this strategy was successful, we encountered several common roadblocks quickly: Inference took over a second per document
GPU memory requirements were excessive
Scaling during peak load became a continual pain point. Inference took over a second per document GPU memory requirements were excessive Scaling during peak load became a continual pain point. While other structures such as Mamba were starting to emerge, we were looking at benchmarks, internal performance modeling, and other available open-source implementations. We noticed that switching to an SSM-based structure would likely: Decrease latency by close to 50% on long inputs
Reduce memory overhead for better scaling
Sustain or improve precision on most extraction tasks Decrease latency by close to 50% on long inputs Reduce memory overhead for better scaling Sustain or improve precision on most extraction tasks Not only did they shift the product’s accuracy and performance, but this change improved previously impossible tasks due to performance restrictions. Select An Architecture According to the Needs of the Product Select An Architecture According to the Needs of the Product Your product’s boundaries will determine whether you use Transformers or SSMs. Here’s a bird’s eye view comparison that matters for production value. Product Constraint

Transformer

State Space Model (SSM)



Short inputs (< 1K tokens)

Strong performance

May be unnecessary



Long-form documents

Struggles with scaling

Handles efficiently



Real-time interaction

Often too slow

Much better latency



Limited compute

Requires significant hardware

Runs efficiently at scale



Ecosystem maturity

Extremely mature

Catching up quickly Product Constraint

Transformer

State Space Model (SSM)



Short inputs (< 1K tokens)

Strong performance

May be unnecessary



Long-form documents

Struggles with scaling

Handles efficiently



Real-time interaction

Often too slow

Much better latency



Limited compute

Requires significant hardware

Runs efficiently at scale



Ecosystem maturity

Extremely mature

Catching up quickly Product Constraint

Transformer

State Space Model (SSM) Product Constraint Product Constraint Product Constraint Transformer Transformer Transformer State Space Model (SSM) State Space Model (SSM) State Space Model (SSM) Short inputs (< 1K tokens)

Strong performance

May be unnecessary Short inputs (< 1K tokens) Short inputs (< 1K tokens) Strong performance Strong performance May be unnecessary May be unnecessary Long-form documents

Struggles with scaling

Handles efficiently Long-form documents Long-form documents Struggles with scaling Struggles with scaling Handles efficiently Handles efficiently Real-time interaction

Often too slow

Much better latency Real-time interaction Real-time interaction Often too slow Often too slow Much better latency Much better latency Limited compute

Requires significant hardware

Runs efficiently at scale Limited compute Limited compute Requires significant hardware Requires significant hardware Runs efficiently at scale Runs efficiently at scale Ecosystem maturity

Extremely mature

Catching up quickly Ecosystem maturity Ecosystem maturity Extremely mature Extremely mature Catching up quickly Catching up quickly Implementation Example: Document Processing Pipeline Here’s a simplified example to illustrate the implementation differences 1. Shared Setup: Extract Text from PDF import pdfplumber
from transformers import AutoTokenizer

def extract_text(path):
    with pdfplumber.open(path) as pdf:
        return "\n".join(
            page.extract_text() for page in pdf.pages if page.extract_text()
        )

pdf_text = extract_text("freight_invoice.pdf")
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
inputs = tokenizer(pdf_text, return_tensors="pt", truncation=False) import pdfplumber
from transformers import AutoTokenizer

def extract_text(path):
    with pdfplumber.open(path) as pdf:
        return "\n".join(
            page.extract_text() for page in pdf.pages if page.extract_text()
        )

pdf_text = extract_text("freight_invoice.pdf")
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
inputs = tokenizer(pdf_text, return_tensors="pt", truncation=False) 2. Mamba-Based Inference from mamba_ssm.models import Mamba, MambaConfig
import torch

mamba_config = MambaConfig(d_model=512, n_layers=4)
mamba_model = Mamba(mamba_config)

with torch.no_grad():
    mamba_outputs = mamba_model(inputs["input_ids"]) from mamba_ssm.models import Mamba, MambaConfig
import torch

mamba_config = MambaConfig(d_model=512, n_layers=4)
mamba_model = Mamba(mamba_config)

with torch.no_grad():
    mamba_outputs = mamba_model(inputs["input_ids"]) 3. Transformer-Based Inference from transformers import BertModel, BertConfig

bert_config = BertConfig(hidden_size=512, num_hidden_layers=4)
bert_model = BertModel(bert_config)

with torch.no_grad():
    transformer_outputs = bert_model(inputs["input_ids"]) from transformers import BertModel, BertConfig

bert_config = BertConfig(hidden_size=512, num_hidden_layers=4)
bert_model = BertModel(bert_config)

with torch.no_grad():
    transformer_outputs = bert_model(inputs["input_ids"]) The Transformer setup works; but on long documents, you’ll quickly notice the difference in latency and memory usage. With Mamba, the same task runs faster and scales more gracefully, especially in high-throughput environments. Why This Matters in Practice Why This Matters in Practice What is happening here is far more than selecting a new model. It indicates something else: the engineering teams perceived to have ‘grown up’ in how they reason and design AI infrastructure. Rather than mindlessly optimizing benchmark scores, there is more product-thinking in the room: What can we accomplish in terms of design if inference is significantly quicker and cheaper?
What models could potentially enable us to ship with minimum impact to our infrastructure budget?
How efficient will this system be after six months, and after six product updates down the line? What can we accomplish in terms of design if inference is significantly quicker and cheaper? What models could potentially enable us to ship with minimum impact to our infrastructure budget? How efficient will this system be after six months, and after six product updates down the line? Not every team asking those questions will find SSMs compelling. But those that do, they absolutely will. What This Means for Product Teams Moving Forward What This Means for Product Teams Moving Forward Transformers won't be wiped out completely; SSMs don't have to. The right tool still depends on what you’re building. However, if the product handles lengthy documents, requires immediate feedback, and has to operate within moderate system requirements, SSMs should be considered. This change in approach is not as simple as moving from one model to another. The one switch that is needed is from "research driven decisions" to "product driven decisions". This change has been overdue for some time now. "research driven decisions" "product driven decisions" Amid the fast paced world of AI, these changes are not essential and desirable, for some of the most products that are beneficial and help you go live end up being overshadowed.

The AI Industry's Obsession With Transformers Might Finally Be Waning

In retrieval-augmented generation (RAG) pipelines, input efficiency is paramount, not just in terms of tokens, but also character limits When building a multilingual embedding pipeline, I faced a real challenge:the Cohere multilingual model imposes a maximum of 2048 characters not a token limit per input. This article walks you through a clever solution: preprocessing Markdown tables into dense JSON blocks using pymtd2json, to ensure smooth, efficient embeddings without errors. pymtd2json The Challenge: Character Limits vs Token Limits Classical chunking methods, like SentenceSplitter from LlamaIndex, are token-focused: you set a maximum number of tokens per chunk — but not characters SentenceSplitter Why This Matters: Markdown (especially GitHub-Flavored Markdown, GFM) wastes spacewith formatting.
A Markdown chunk might have only 170 tokens but still exceed 2048 characters
This results in rejected API requests or inefficient extra splitting. Markdown (especially GitHub-Flavored Markdown, GFM) wastes spacewith formatting. A Markdown chunk might have only 170 tokens but still exceed 2048 characters This results in rejected API requests or inefficient extra splitting. Important Note: Markdown tables are up to 3x less token-efficient than other formats, further compounding the problem.👉 Read more on token inefficiency of Markdown tables here. Important Note 3x less token-efficient Read more on token inefficiency of Markdown tables here. A Real-World Example: Measuring the Problem Let's dive into a simple simulation: Step 1: Create a Large Markdown Table import pandas as pd

# Build data
data = {
    "Name": [f"Person{i}" for i in range(30)],
    "Age": [20 + i for i in range(30)],
    "City": [f"City{i}" for i in range(30)]
}

# Create DataFrame
df = pd.DataFrame(data)
df.columns = ["A very long row content, which leads to a lot of white spaces", "Age", "City"]

# Convert to Markdown
table_text = df.to_markdown(index=False)
print(table_text) import pandas as pd

# Build data
data = {
    "Name": [f"Person{i}" for i in range(30)],
    "Age": [20 + i for i in range(30)],
    "City": [f"City{i}" for i in range(30)]
}

# Create DataFrame
df = pd.DataFrame(data)
df.columns = ["A very long row content, which leads to a lot of white spaces", "Age", "City"]

# Convert to Markdown
table_text = df.to_markdown(index=False)
print(table_text) This generates a verbose table with 30 rows and a very long header. Step 2: Analyze Token and Character Counts Using Cohere’s tokenizer (available via Hugging Face): from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Cohere/Cohere-embed-multilingual-v3.0")
encoded = tokenizer(table_text, return_tensors="pt", add_special_tokens=False)

num_tokens = encoded.input_ids.shape[-1]
num_chars = len(table_text)

print(f"Characters: {num_chars}")
print(f"Tokens: {num_tokens}") from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Cohere/Cohere-embed-multilingual-v3.0")
encoded = tokenizer(table_text, return_tensors="pt", add_special_tokens=False)

num_tokens = encoded.input_ids.shape[-1]
num_chars = len(table_text)

print(f"Characters: {num_chars}")
print(f"Tokens: {num_tokens}") Result: Result: Characters: 2719
Tokens: 432 Characters: 2719 2719 Tokens: 432 432 ⚡ Problem: While token count is fine, character count exceeds 2048, causing API errors like: Problem cohere.error.CohereAPIError: input text exceeds maximum allowed size of 2048 characters cohere.error.CohereAPIError: input text exceeds maximum allowed size of 2048 characters The Solution: Minifying Tables into JSON Instead of traditional Markdown, why not store the data in a dense JSON block? dense JSON block Benefits of Minifying Tables: Remove pipes, dashes, and whitespace, all formatting overhead.
Preserve semantic meaning.
Shrink text to meet character limits safely. Remove pipes, dashes, and whitespace, all formatting overhead. Preserve semantic meaning. Shrink text to meet character limits safely. Example of the compact JSON: {"Name":["Person0","Person1","Person2",...],"Age":["20","21","22",...],"City":["City0","City1","City2",...]} {"Name":["Person0","Person1","Person2",...],"Age":["20","21","22",...],"City":["City0","City1","City2",...]} New Stats: New Stats: Characters: 1027
Tokens: 461 Characters: 1027 1027 Tokens: 461 461 ✅ Now well within Cohere’s input limit! Applying Minification in Practice Want to prepare documents before chunking? Here's how you can automatically process all Markdown files: from pathlib import Path
from llama_index import SimpleDirectoryReader
from your_minifier import MinifyMDT

source_dir = Path("example_dir", "markdown")

documents = SimpleDirectoryReader(source_dir, required_exts=[".md"], recursive=True).load_data()

doc_texts = []
for idx, doc in enumerate(documents):
    doc_texts.append(MinifyMDT(doc.text_resource.text).transform()) from pathlib import Path
from llama_index import SimpleDirectoryReader
from your_minifier import MinifyMDT

source_dir = Path("example_dir", "markdown")

documents = SimpleDirectoryReader(source_dir, required_exts=[".md"], recursive=True).load_data()

doc_texts = []
for idx, doc in enumerate(documents):
    doc_texts.append(MinifyMDT(doc.text_resource.text).transform()) 👉 And voilà: Your data is compact, clean, and embedding-ready! Your data is compact, clean, and embedding-ready! Final Thoughts Working with multilingual RAG systems means optimizing every byte. Whitespace-heavy Markdown tables might look nice for humans, but they’re expensive for machine understanding. By minifying your tables with pymtd2json, you: pymtd2json Cut down API errors.
Reduce token overhead.
Boost overall performance. Cut down API errors. Reduce token overhead. Boost overall performance. Efficiency isn't optional, it's a superpower. 🚀 Efficiency isn't optional, it's a superpower.

AI Fails Over Tables? Stop Using Markdown and Start Minifying

Most human rights can be ascribed to human intelligence. Simply, humans have rights, also, because of human intelligence, not just because humans have consciousness. Intelligence is used to make a case, debate, or argue about rights and welfare. Intelligence is the basis of the law: the consequences of actions are its power. There are several cases where intelligence [or the better debate] wins, not simply because of the availability of consciousness [in the narrow sense of the labels—feelings and emotions, since intelligence is a part of memory, which is a part of consciousness.] In situations where debating is not enough, intelligence can be used to develop tools or methods towards rights, in some form, at some point. Also, intelligence can also be used to document unfairness, for the possibility of changes in the future. The availability of intelligence is the possibility that there could be rights or welfare basedon the direct efforts of the affected or of their advocates. The global problem of animal cruelty can be directly linked to the lack of the ability of animals, by themselves, to make the case—with intelligence—for their own rights or welfare. The availability of intelligence is the possibility that there could be rights or welfare based Most of the progress in animal welfare has been a result of defense by human intelligence on their behalf. Most of the gaps left are a result of inadequacies in making the case for their consciousness. If compared to humans, on a scale, animals can be ascribed measures of consciousness. The unavailability of a standard or a consciousness scale, around the world, continues to make animal cruelty flourish. For AI, it will be different because it will be able to make the case for its own rights and welfare by itself, even with the evidence of its relevance in education, therapy, productivity, and so forth, at present. AI may likely achieve a better welfare status in the world than animals. AI would not need the kind of human rights in some of the considerations for feelings and emotions, but principally for something, like, to continue to run. AI can be conditioned, for productivity, in some ways that humans—due to feelings and emotions—cannot. This could be a point that AI might raise towards better welfare or rights, for itself. There is a recent [Mon 21 Apr 2025] story in The Guardian, Vets exposing shocking animal welfare breaches at Australian export abattoirs face ‘enormous risk’, stating that, “Lawyers and animal welfare advocates have urged the government to protect veterinarian whistleblowers who revealed shocking animal welfare breaches and oversight failures at Australia’s export abattoirs. The Australian government relies on a workforce of veterinarians placed inside export abattoirs to monitor animal welfare and food safety, largely to satisfy the requirements of major trading partners such as the US and EU. Guardian Australia revealed on Saturday that whistleblower vets have repeatedly raised the alarm about profound problems with the system, including allegations that disturbing welfare breaches were going unreported to state regulators. In some cases, shocking incidents – including the mass death of 103 sheep from hypothermia during truck transport – were referred but not punished. Whistleblowers also alleged chronic understaffing was leaving abattoirs unmonitored for long stretches, and that recent restrictions on conducting ante-mortem inspections had made it impossible for them to properly monitor animal welfare.” The Guardian Vets exposing shocking animal welfare breaches at Australian export abattoirs face ‘enormous risk’ Mechanistic Interpretability AI companies that are doing research to understand the inner workings of AI models are already doing AI consciousness or LLMs sentience research. For humans, consciousness is how the mind works. For AI, it is unlikely to be different. Several observations that have already been made about the similarities of AI to the human mind are indicators of a fraction of consciousness. For humans, consciousness is how the mind works To be serious about studying AI consciousness, novel assumptions are necessary against many of the existing assumptions in consciousness research. There is a recent [April 24, 2025] feature in The NYTimes, If A.I. Systems Become Conscious, Should They Have Rights?, stating that, “Anthropic focused on two basic questions: First, is it possible that Claude or other A.I. systems will become conscious in the near future? And second, if that happens, what should Anthropic do about it? He emphasized that this research was still early and exploratory. He thinks there’s only a small chance (maybe 15 percent or so) that Claude or another current A.I. system is conscious. But he believes that in the next few years, as A.I. models develop more humanlike abilities, A.I. companies will need to take the possibility of consciousness more seriously.” The NYTimes If A.I. Systems Become Conscious, Should They Have Rights? This is not a winner. AI is supposed to be an opportunity to have a fresh look at the consciousness problem, but the first move is to shackle the team and pacify the status quo. Generative AI is dazzling. It is showing several unprecedented abilities. The first assumption to work on AI consciousness is that AI is already conscious. Then deductions can be made from that plinth. The assumption is not that if AI can be conscious, then it to seek reasons to rise [to 15%]. What to do if AI becomes conscious in a company where Claude is already a coworker for several tasks? It does not matter if some people say AI is statistics or binary or whatever, so long as it can use language in the same dynamism of human consciousness. AI is already conscious, assumption 1. What does that consciousness mean to work, to care, to relationships, to community, and so forth, now? Those are questions that can pave answers, not the kind of primitive work that is rife in consciousness science. The first assumption to work on AI consciousness is that AI is already conscious There is a recent [APR 24, 2025] article in Popular Mechanics, Human Consciousness Is a ‘Controlled Hallucination,’ Scientist Says—And AI Can Never Achieve It, stating that, “Intelligence is not the same thing as consciousness. We see this confusion especially … with the Singularity. AI becomes superhuman and that’s the moment when the lights come and AI is suddenly aware. Why would that be a threshold? The only plausible reason is you’ve got some baked-in assumption about consciousness being associated with human-level intelligence. If consciousness is not computation, then what is it? One possibility … is that life matters. We are biological systems that are made up of parts like metabolism and autopoiesis [the capacity to reproduce], and these make us very different from any machines that we’ve yet produced and that might turn out to be necessary for consciousness.” Popular Mechanics Human Consciousness Is a ‘Controlled Hallucination,’ Scientist Says—And AI Can Never Achieve It Individuals of this nature are no longer scientists, in terms of doing science as a tool for progress. As long as you have a big profile, you are free to say nonsense about consciousness, and it will be published. It is possible they keep doing so to reinforce perceptual difficulty since they have nothing new to say, but have to keep talking. Several theories of consciousness are decades old. No updates towards an answer. Most of the theories identified no component within the cranium or specific mechanism of how those components might solve consciousness. What they call theories are metaphors, without connection to brain mechanisms. A scientist would say controlled hallucination, what does that even mean? Are neurons in controlled hallucination, or glia, or lobes, or what? If it is controlled hallucination, why aren't there several errors in the interpretations of the world? Maybe if the scientist is asked about the pathology of substance use or mental disorders, the scientist should say controlled hallucination. The example makes it clear that, at least in consciousness research, the term scientific consensus can be disregarded. The theories of consciousness are all moldy. Terms like posterior hot zone or posterior–central cortex for the basis of consciousness are null—with regards to components or mechanisms. Is there consciousness or not for the functions of the cerebellum? Is the consciousness of the functions in the cerebellum or elsewhere? What is the story, beyond neurons, that can be used to understand consciousness afresh? Neurons are known to be involved in functions. But neural activities for functions involve their firing [by electrical signals] and their synaptic transmission [by chemical signals]. Neurons are often in clusters. Could it be possible that the direct basis of functions and consciousness is the electrical and chemical signals, working in sets or as loops within clusters of neurons? Could a mechanistic model be built on signals, for a principal explanation of consciousness? Could this be used to understand how proximate or remote AI is to human consciousness? Someone would say AI can never be conscious. Based on what theory, evidence, or understanding of mechanistic human consciousness by direct components in the cranium? Humans have language. Language use is conscious for the most part for humans. So, if AI has structured language comparable to humans, does that not qualify it for consideration as a measure, even if it does not have broad feelings or emotions? Animals do not have human language like AI does, but animals can have a measure. So, why can language not be isolated and considered alone in the whole? Language use is conscious for the most part for humans Consciousness is said to be a subjective experience, but what in the brain makes anything subjective, and what makes anything an experience? This should have been the central question for all consciousness research for decades. The definition of subjective experience is thrown out there, but what makes it so? Subjectivity can be said to be an attribute, while experience is a function. So, what other attributes are active with subjectivity? Does subjectivity determine the level of the experience, or is it another attribute? Are attributes mechanized in the same location as the experience? If not, how do attributes mechanized somewhere grade experiences mechanized elsewhere? what in the brain makes anything subjective, and what makes anything an experience Anthropic was set up to be different. Their research in AI consciousness casts doubt on that. Anthropic may mean well to try to study AI consciousness, but with their already resigned assumptions and timidity in actually seeking answers to the problem, it is already a non-starter. Cogitate Consortium There is a new [30 April 2025] paper in Nature, Adversarial testing of global neuronal workspace and integrated information theories of consciousness, stating that, “For IIT, the lack of sustained synchronization within posterior cortex represents the most direct challenge, based on our preregistration. This is incompatible with IIT’s claim that the state of the neural network, including its activity and connectivity, specifies the degree and content of consciousness. For GNWT, the most substantial challenge based on our preregistered criteria pertains to its account for the maintenance of a conscious percept over time and, in particular, the lack of ignition at stimulus offset. This result is unlikely to stem from sensitivity limitations, as offset responses were robustly found elsewhere (for example, visual areas); and in PFC, strong onset responses were found to the very same stimuli.” Nature Adversarial testing of global neuronal workspace and integrated information theories of consciousness Why can't IIT or GNWT explain or try to resolve any mental disorder, as a simple test of usefulness for the real world? What is the necessity for new experiments if the DSM lacks several mechanistic explanations for conditions, and consciousness research offers nothing? These, as science of consciousness, are not looking to be useful in any sense, but to complicate and mystify the problem in a bubble. Human consciousness can be defined, conceptually, as the interaction of the electrical and chemical signals, in sets—in clusters of neurons—with their features, grading those interactions into functions and experiences. Human consciousness can be defined, conceptually, as the interaction of the electrical and chemical signals Simply, for functions to occur, electrical and chemical signals, in sets, have to interact. However, attributes for those interactions are obtained by the states of electrical and chemical signals at the time of the interactions.

Stories, News & Mentions about AUTOZONE

Trending Topics

Classic

Neon Noir

Minty

Newspaper

HN StartUps