Podcasts about extracting

"Sales and marketing just seems to be like a lost skill  so you have to learn to be the differentiator as the human bridge." This is a special episode only available to our podcast subscribers, which we call The Mini Chief. These are short, sharp highlights from our fabulous guests, where you get a 5 to 10 minute snapshot from their full episode. This Mini Chief episode features Nick Lissette, Founder & CEO of Blackpearl Group. His full episode is titled Driving data into dollars, building human bridges in an AI world, and his "win or die" attitude. You can find the full audio and show notes here:

In this week's Podcast: Wouldn't you just know it, on the one day I really needed to carry out some inspections, it's rained. It was cold, and it just kept raining all morning, the result, next day, yep, swarming!Hi, I'm Stewart Spinks, welcome to Episode 395 of my podcast, Beekeeping Short and Sweet.Please support us throught affiliate links below, they cost you nothing and help us continue to produce our content.Nucleus Colonies For SaleBeekeeping Courses at Thorne Beehvies in Wragby Lincolnshire 2026Thorne Beehives Bailey BoardSome of my Favourite Microscopy Books:Pollen Loads of the Honeybee by Dorothy HodgesRex Sawyer's Pollen IdentificationPollen Grains and Honeydew by Margaret AdamsThe Pollen Landscape by Joss BartlettPollen Microscopy by Norman ChapmanThe National Bee Unit Varroa Information can be found HEREBee Aware Varroa Information can be found HEREThorne Beehives Bees on a Budget Hive The Beekeeper's Dictionary websiteEthyl Acetate for colony destructions can be found hereGardening Potting Tray for effective frame cleaningStainless Steel Stock Pots for use as a double boiler. Get one slightly larger than the other to fit inside.Gas Stove for outdoor use to render wax and old comb.Contact Me at The Norfolk Honey CompanyVMD Website: Click HEREJoin Our Beekeeping Community in the following ways:Early Release & Additional Video and Podcast Content - Access HereStewart's Beekeeping Basics Facebook Private Group - Click HereTwitter - @NorfolkHoneyCo - Check Out Our FeedInstagram - @norfolkhoneyco - View Our Great PhotographsSign Up for my email updates by visiting my website hereAmazon links are affiliate links. I recieve a small commission should you choose to purchase.Support the show

Emerging markets are becoming a strategic focus for creditors looking to extract the complexity premium embedded in local markets. Atanas Bostandjiev, chairman and founder of Gemcorp Capital Management, joins Damian Sassower, Bloomberg Intelligence's chief EM fixed income strategist, to assess the opportunities available to institutional investors with perpetual capital. Bostandjiev and Sassower discuss the durability of alpha generation, the formation of behavioral biases and the ability to capitalize on structural inefficiencies across EM.

This show has been flagged as Clean by the host. 01 This is the fourth episode in a four part series on simple podcasting. 02 Introduction In this episode we will discuss alternatives to Audacity when it comes to analyzing audio spectrums to find the sources of unwanted noise. I previously promised some gratuitous hackery, and we will get into that in this episode. 03 Recall that with Audacity you first import the audio file, then select the part of the audio you wish to analyze (or ctrl-A for all), and then select analyze > plot spectrum. This is in fact the only feature of Audacity that I know how to use. I am definitely not an audio expert. I do however have some background in processing and analyzing other signals, so some of the basics are familiar to me. 04 We can accomplish the same thing that Audacity does in this instance provided we can do the following. First, we need to get the data out of the audio file and into a form which we can import into other software. Second, we need to perform certain mathematical operations on this data. Finally, we need to be able to plot the results of these calculations on a chart. -------------------- 05 Fourier Transforms First though, we need a bit of mathematical background. What Audacity is doing when it shows a plot of frequency versus amplitude is that it is showing the results of a Fourier Transform. A Fourier Transforms is a mathematical operation that converts the time domain into the frequency domain. Any complex signal, audio or otherwise, can be broken down into a collection of sine waves of various frequencies. For example, a simple square wave signal of say 100 hertz can be represented as a sine wave of frequency 100 hertz plus a collection of higher frequency sine waves which add together to give the sharp corners. 06 A Fourier Transform finds these sine waves and sorts them out into separate bins, with each bin representing an individual frequency or a collection of closely related frequencies, depending on how fine grained the sorting is. 07 This is exactly what we want when we are trying to figure out how to filter out noise. Recall that earlier in this series we had to solve a problem with a high pitched background noise which was originating in my cheap microphone. Analyzing this audio by frequency showed that it was a series of individual tones at 1 kHz intervals. We were then able to use filters targeted at those frequencies to get rid of that noise. 08 There are several optimized versions of the Fourier Transform algorithm. A very common one is the Fast Fourier Transform, common abbreviated to just "FFT". This is so common that the term "FFT" is often used to simply mean any Fourier Transform even though this is not technically correct. 09 Typical FFT algorithms require that the number of data samples is exactly a power of two. So the number of samples we need may be something like 4096, 8192, or 65536, to give a few random examples. When we transform from the time domain to the frequency domain, each sample becomes a single frequency "bin". So the more samples we have, the finer the resolution we get in terms of frequency. 10 If we assume we are dealing with flac files recorded at a 44.1 kHz sample rate, that is, 44100 samples per second, then if we have 32768 samples, each "bin" represents slightly more than 1 hertz. If we have 65536 samples, then each "bin" represents a fraction of a hertz. For our purposes we will pick 65536 samples. That means we need 1.48 seconds of data. For simplicity's sake we will record at least 2 seconds of data and then just discard the samples that we don't need. 11 There is a further complication here. Fourier Transforms normally work with complex numbers. Recall from your school days that as well as integers and real numbers there are complex numbers. Each complex number consists of two parts, a real component and an imaginary component. I won't go into the details of this, just accept that each sample needs to have two components. Fortunately, if we don't have complex number data we can just set the imaginary component to zero and use that. This is enough talking about the theory, let's get into the practical details. -------------------- 12 Extracting Data from Audio Files First we will look at how to extract the data from the audio files. Fortunately, one of the programs which we have already been using can do this. To do this we will use Sox. I am not aware of an equivalent feature in ffmpeg. 13 Sox calls itself "SoX - Sound eXchange, the Swiss Army knife of audio manipulation" Sox is free software and is licensed under the GPLV2 or later. In this case we want to use a feature which allows us to convert a binary audio signal file to a text data file. To convert the file to text data we just give the output file a ".dat" file extension and Sox will do this for us. 14 Here is a command example. sox inputfile.flac tdata.dat 15 This gives us a file in the following format, assuming this is a mono audio recording. ; Sample Rate 44100 ; Channels 1 0 0.045471191406 2.2675737e-05 0.055023193359 4.5351474e-05 0.048217773438 6.8027211e-05 0.053192138672 etc. The first line states the sample frequency The second line states that the data is for channel 1. The data starts on the third line. Column 1 is the time in seconds. Column 2 is the waveform data point. 16 To analyze the data we want a subset of these samples. When we convert from the time domain to the frequency domain, our resolution will be determined by the number of samples. We would like therefore to have at least as many samples as the sampling rate. We also want the samples size to be an even multiple of two. The number of points we want to have is equal to the next even multiple of two above our chosen sampling rate, 44,100 Hz. This number would be 65536. 17 To extract this data from the file we can do the following. tail tdata.dat -n+3 | head -n65536 | awk '{printf "%sn", $2}' > tdata.csv 18 We use tail to skip over the first three lines. We use head to take the next 65536 lines and discard the rest. We use awk to extract the second column which we will use as the real component. We now have this data as a csv file in one column. -------------------- 19 Analyzing the Data To analyze the data we need software which can calculate FFTs. I will now show two examples of this, a very simple case using Libre Office Calc, and a more complex but more complete one using GNU Octave. 20 Using Libre Office We can do fourier analysis and plot charts using Libre Office. Take the csv file of data that we previously created. For this example I used data from a recording of silence so that I could see what internal noise was being generated by the headset. Open the csv file and import it into Libre Office Calc. 21 Now select all 65536 rows of column A. The Fourier function will automatically fill the imaginary component with zeros if we don't provide an column of imaginary numbers, so we don't need to provide a column of zeros. Then select Data > Statistics > Fourier Analysis. 22 A window will open allowing you to select various parameters. For Results to:, enter "D1". Grouped by Columns. Select OK. 23 New data should now appear starting in cell D1. The first line will say " Fourier Transform" The second line will state the input range. The third line will state "Real" in column D, and "Imaginary" in column E. The data will start in row 4. 24 For our simple example we will ignore the imaginary data and just use the real data, which will form our Y component when we plot it on a chart. We now need to create the X axis data. 25 Each cell is a "bin" of frequencies. Each cell therefore represents (sample frequency) / (Number of samples) Hz. 26 To create the X axis data showing frequency, enter the following formula in to column C to the left of each D column number. =((44100/65536) * (ROW() - 4) 27 We can now create an XY chart showing the frequency analysis. You may need to exclude the first couple of dozen rows as very low frequency components which cannot be heard may otherwise overwhelm the data we are interested in. Also, you only need the first half of the chart. The FFT mirrors the data from the first half of the array into the second half. 28 Because characterizing a sine wave requires a minimum of 2 points, although we have a sample frequency of 44.1 kHz, we really only have sound waves up to a maximum of half that, or 22.05 kHz. Create the chart with lines only. If you followed the above instructions, you should see something resembling what we saw in Audacity, except with each bin more sharply defined. 29 In the data that I had from a recording of unfiltered headset noise, I could see a distinct noise spike every 1000 hertz. 30 However, we have taken several shortcuts. First, the imaginary component of the data was ignored. Second, the magnitude (that is, Y axis) has both positive and negative peaks. Third, the data is not scaled to dB sound units, so we just have a relative measure. However, that by itself is enough to tell us where the frequencies are that we need to construct filters to deal with. 31 We could refine this spreadsheet a bit more to deal with the above issues, but I think we have demonstrated the basic principle, and working with a spreadsheet can be a bit awkward. However, if working with a spreadsheet is what you want to do, then you can add more columns and more formulae to improve on it. -------------------- 32 Other Analysis Software I will go on to GNU Octave in a moment, but I want to get a few other alternatives out of the way first. I won't go into any detail on them other than to point them out to people who want to have a go at trying these themselves. 33 Grace There is math and plotting software called Grace. This is free software, released under the GPL V2. According to the documentation, it seems to have the features we need, including an FFT function. However, I could not get it to work properly on Ubuntu 24.04. I could not get it to load a data file and plot data. 34 The error messages were vague and unhelpful. The file navigation system didn't work. There was no obvious path to success, and if it isn't easy to use then there is no point to it. This is fairly old software, designed for X Window and Motif. I gave up on it as not suitable for this series as I am looking for some fairly low effort things for people to try themselves. If someone else can get it to work on their PC, perhaps they could do an HPR episode on this themselves. 35 Command Line FFT Packages There are several command line FFT packages. They will read data from std in or from a file and output the FFT. However, these are not packaged for Ubuntu and appear to be distributed as C source code which you would download and compile. You can experiment with those if you wish, but I felt they were a bit out of scope for discussion here as I am looking at common tools that are ready to use. 36 Here are two examples. One is Command-line Fast Fourier Transform utility https://github.com/gregfjohnson/fft Another is cli-fft https://github.com/jonolafur/cli-fft 37 I have not tried these and cannot say whether they are any good or not. Similarly, there are a number of FFT packages that are libraries for languages such as Python. If you want to take the time to write a short program to go with them, you can create a dedicated FFT command line program. However, I felt that this too was out of scope for what I was trying to do here. 38 Doing it the Hard Way Hypothetically, it may be possible to write an FFT function in bash bc, which is the arbitrary precision calculator language which is part of the standard shell package. I say hypothetically, because I have not tried it. I think it would be an interesting challenge, but I don't have the time at the moment to try it. If anyone feels motivated to give it a try, they're welcome to give it a go and then do a podcast episode on it. -------------------- 39 GNU Octave We have seen that as well as using features built into Audacity to analyze the audio spectrum to see the frequencies of undesired noises, we were able to do the same using a Libre Office spreadsheet. 40 Now we'll look at another bit of software, GNU Octave. GNU Octave is free software, licensed under the GPL V3 or later. It is a mathematical scripting language, very similar to Matlab. People use it for mathematical, engineering, and scientific work. It can be found in most Linux distros and is available for some other operating systems as well. 41 Octave has two features built in that we need for our purposes. It does FFTs, and it has a plotting system built in to produce graphs. -------------------- 42 We will take the same audio test file that we used with Audacity and Libre Office and use it here as well. The bash script to convert the flac file to text data is essentially the same, with the exception that file extension on the output file as is ".txt" instead of ".csv". This latter change was an arbitrary decision on my part. 43 As a quick review, this bash script uses sox to convert a flac file to a text ".dat" file. Then it uses tail, head, and awk to extract the first 65536 rows of data, skipping over the header information and ignoring the first column of time data. This script will be in the show notes. -------------------- #!/bin/bash # This version is for use with the GNU Octave script. sox hsnoisemono.flac hsnoisemono.dat tail hsnoisemono.dat -n+3 | head -n65536 | awk '{printf "%sn", $2}' > hsnoisemono.txt -------------------- 44 We now have a 1.1 MB file containing 65536 samples of data in text format. Now the next thing we need to do is to create a short Octave script file. I will just give a brief overview of the script here, the full script will be in the show notes. 45 I put the script in a file called "octavespectrum.m". I have never used Octave before now, but the convention seems to be to give the script a ".m" ending. The "she-bang" line is "#!/usr/bin/env octave". If you make the file executable you can run it like any other script, or you can type "octave" and then the name of the script to run. 46 I won't read out the script in detail, as that would be too hard to following along in a podcast. However, I pass several arguments to the script including the name of the data file, and then two integers that I use to limit the display area in the Y and X axes so I can have the chart focus on the areas of interest that I want to see. I also pass a string containing the name of the graphic file that I want the chart exported to. This was an arbitrary decision on my part and you can just hard code these values in if that is what you want to do. 47 The arguments are accessed by calling the "args()" function, which returns an array of strings. Next, it reads in the specified file using the "dlmread()" function. This reads all of the data into an array. 48 Next, it performs a hamming windowing function on the data. I'll explain that briefly. It is standard practice when doing FFT signal processing to "window" the signal. Since the signal sample is of finite length, it will stop at each end of the array. 49 Unless you were lucky enough for this to happen exactly at a zero crossing, this would produced an abrupt transition in the data which looks like "noise" to the FFT. The solution is to taper the signal off gradually towards the ends so that when it gets cut off the signal is fairly small at that point anyway. There are a variety of different windowing functions, but "hamming" seems to be the most commonly used. 50 Next, it does an FFT using the "fft()" function. 51 This gives us real and imaginary outputs. These are combined by summing the squares of each corresponding real and imaginary element and then taking the square root of each and storing that in a new array. This gives a single array of the same length as the originals, but combining the two output components. If anyone wants to tell me that this isn't how things are done in the audio world, they're welcome to make an HPR episode telling us all the right way to do things. 52 Then it does some scaling and selection of subsets of data so we get the X axis in hertz and just the number of samples that we wish to look at. If you are looking at the script, the thing to keep in mind is that Octave will work on entire arrays of data in a single operation. You don't need to write explicit loops for this. The looping is handled implicitly as part of the syntax. 53 It also does various other things that make the chart easier to read. The comments in the script describe these in more detail. Since this is a script it's easier to add these sorts of refinements than is the case for a spreadsheet so I have made the effort to add them. Finally it calls the "plot()" function. If an output graphics file name was provided, it also creates a PNG file containing the same image using the "saveas" function. 54 We now see the chart, and it looks more or less as expected. However, this chart is interactive. You can zoom and pan the data, something that you can't do with either Audacity or Libre Office. The chart window doesn't have a function for exporting the resulting chart to a "png" file, it will only save to an ".ofig" file. The ofig file is not a standard graphics file, it is a serialization of the chart data that can only be looked at using the Octave chart viewer. 55 Alternatively, you can just take a screenshot of the chart after you have interactively zoomed and panned to a point of interest. At the bottom left of the chart window is a pair of x-y coordinates which tell you the current position of the mouse pointer in chart units. This is very handy as it can be used to get the exact (or close to exact) frequency of each noise spike. 56 The Y axis is not scaled in any particular units such as dB, as I'm not sure how to do that according to audio industry conventions. On the other hand, I'm not sure that it's really necessary, as I don't know what dB means in tangible terms anyway. It does show relative sizes, so it helps to determine whether you have one noise frequency or multiple frequencies to worry about. 57 If anyone is familiar with how to scale the raw data from a flac file as exported by Sox into dB units according to audio industry convention, then they are welcome to create an HPR episode telling us how to do it. -------------------- 58 Comments on GNU Octave I had never used GNU Octave before this, although I had heard of it and it is quite a significant piece of software for a specific segment of users. 59 The syntax is a bit odd especially in how it deals with array operations, but I was able to google various examples and answers to eventually get this working. A few other peculiarities are that it uses the percent "%" character to denote a comment, and leaving out the semi-colon at the end of the line causes it to print the answer to the console after executing the statement. 60 The GNU Octave solution was harder to get working than the Libre Office method. However, once it was working it is easier to use repeatedly. If I were to want to automatically generate audio files with different filtering or other options and wanted to script the creation of a large number of images showing the results, this would be the way to do it. 61 When your run the Octave script you may get a warning which says something like "QSocketNotifier: Can only be used with threads started with QThread". This is apparently a routine warning message from the Qt graphics system which has no real significance in this context and can be ignored for our purposes. -------------------- 62 We now have a bash script which will use sox to extract the data from a flac file, and a GNU Octave script which can be used to display the resulting frequency spectrum. This does more or less the same thing as "Plot Spectrum" does in Audacity, but allows for zooming and panning to get a more detailed look at the data. 63 However it doesn't give you an absolute reading of the sound levels in dB, something that Audacity does provide. What I wanted it for though was to find the frequencies of the audible noise in the signal, something that it does quite well. -------------------- #!/usr/bin/env octave % Perform an FFT on the data in a file and plot the results. % ====================================================================== % The sampling frequency. This must be changed to accommodate the % actual sampling frequency if it was something else. samplefreq = 44100; % Thickness of line on plot. linewidth = 2; % ====================================================================== % The name of the data file is passed as a argument. args = argv(); if length(args) < 3 quit endif % File name. fname = args{1}; % Clip the peak values. peakclip = str2double(args{2}); % How much data to show, in kHz. rbound = str2double(args{3}) * 1000; % The optional file name to save a chart image to. if length(args) > 3 chartfile = args{4}; else chartfile = ""; endif % ====================================================================== % Read the data in from the file. sampledata = dlmread(fname); % Number of samples. samplecount = length(sampledata); % ====================================================================== % Window the data. This helps deal with the discontinuity of data at % each end of the array and the effects this has on introducing apparent % noise into the signal. windoweddata = (hamming(samplecount) .* sampledata); % ====================================================================== % Do the actual FFT. fftresults = fft(windoweddata); % Get real component. r = real(fftresults); % Get the imaginary component. i = imag(fftresults); % Combine the real and imaginary. In order to square each element of each % array, we must use the ".^" operator, not just "^". rfft = sqrt(r.^2 + i.^2); realfft = rfft(1:samplecount); % ====================================================================== % Scale factor for frequency. fscale = samplefreq / samplecount; % X axis scale, scaled to frequency. f = (0:samplefreq/2) * fscale; % Take a subset of the data if specified. rbound has to be re-scaled % from kHz to array increments. freq = f(1:min(rbound / fscale,length(f))); % y axis. We take the absolute value and then limit (clip) the peaks % so that a few large peaks don't obscure the smaller ones. mag = min(abs(realfft(1: length(freq))), peakclip); % Plot the results. figure; whandle = plot(freq, mag, 'LineWidth', linewidth); title(["Audio Spectrum of ", fname]); xlabel("Frequency (Hz)"); ylabel("Unscaled Magnitude"); grid on; % If the appropriate optional argument was specified, save the chart % to a file of that name. if length(chartfile) > 4 saveas(gcf, chartfile, "png"); endif % Need this so the plot window stays open. waitfor(whandle); % ====================================================================== -------------------- This is the shell script used with the above Octave script. The arguments are 1 - the file name for the input data file. 2 - The value to clip the peaks at. 3 - The upper frequency bound in kHz. 4 - The output graphics file name. #!/bin/bash octave octavespectrum.m hsnoisemono.txt 10 12 hsnoisemono.png -------------------- 64 Episode Conclusion In this episode we covered the following topics. What Fourier transforms are. Extracting data from audio files using Sox. Analyzing the data using Libre Office. Analyzing the data using GNU Octave. And, several alternative analysis methods. 65 Series Conclusion This is the end of a four part series on simple podcasting. In the first episode, we covered a simple podcast recording method. This first episode is all you really need to make a podcast. 66 In the second episode we covered basic filtering and a few other simple topics. The methods discussed in that episode provide basic improvements to your audio if you feel the need for it. 67 In the third episode we covered how to analyze audio noise problems using Audacity and additional filtering techniques to deal with specific problems that we may find. We also covered command line recording, playback, and getting information about an audio recording. 68 In the fourth episode we engaged in a bit of gratuitous hackery for the fun of it and showed how to use alternative software methods to analyze audio signals. 69 I hope that this series has been both useful and entertaining and that you will use the knowledge gained here to create and submit your own HPR podcast episodes. -------------------- -------------------- Provide feedback on this episode.

"Is your customer winning from using your product? Are they making or saving money from you? If so, how much and how is that measurable to your product?" In this episode of The Inner Chief podcast, I speak to Nick Lissette, Founder & CEO of Blackpearl Group, on Driving data into dollars, building human bridges in an AI world, and his "win or die" attitude.

*Cattle futures trading limits are expanding.  *Hurricane season gets underway soon.   *China is expected to buy $17 billion in agricultural products from the U.S.  *Southeast Texas County Farm Bureaus hosted a Legislative Ag Day last week.  *Cotton and sorghum seem to be the best options to grow in the Texas High Plains this year.  *A few fertilizer plants have been damaged by the conflict in the Middle East.  *Farmers on the Texas Southern Plains need rain to get spring crops in the ground.  *Extracting a horse tooth is not an easy job.  

Your Story Matters: Extracting the Gold Nuggets What if the hardest parts of your story are actually where the gold is buried? In this inspiring and heartfelt episode, Stacy shares how our pain, setbacks, grief, and rebuilding seasons can become the very things that prepare us to lead, heal, serve, and create impact. Through powerful stories, leadership principles, and personal experiences — including the loss of Olivia, health challenges, healing, and transformation — Stacy explores how true leaders use their stories to light the way for others. This episode is a reminder that: your story matters, your pain is not wasted, and your best chapters may still be ahead of you. In This Episode: Finding the “gold nuggets” in your story Why suffering often prepares us for our calling Lessons from Abraham Lincoln and great leaders Turning pain into purpose and impact Midlife as a season of awakening and rebuilding The importance of creativity, growth, and sharing your gifts Why transformation stories help others heal Favorite Quotes: “Sometimes the gold nugget isn't what happened to you. It's who you became because of what happened to you.” “Character is forged before the calling arrives.” “The world does not need a perfect version of you. It needs the transformed version of you.” If this episode encouraged you, please share it with someone who may need hope today. Your story is not over.The best may still be ahead. www.stacyharmer.com www.comewhatmay.world

Avani Adhikari — head of insights at GatherGov — is in good traffic this week for a conversation about mapping America's data center development pipeline, what happens when you analyze millions of hours of municipal meeting recordings, and the most controversial topics in local government. As cities hear proposals they don't fully understand and residents voice opposition to infrastructure they've never encountered before, Avani's work sits at the intersection of technology, and civic engagement.Avani walks through how GatherGov listens to and analyzes meetings from over 7,800 jurisdictions to extract entitlements data and assess development risk. She explains the sharp uptick in data center construction spending since 2022 — a timeline that directly correlates with ChatGPT and large language models — and breaks down why these projects spark such intense community opposition despite their often-invisible presence. From water usage concerns to property value fears to fundamental questions about who benefits from AI infrastructure, the conversation explores how communication gaps between tech companies and residents create friction that could be avoided. She also discusses why the Northeast consistently shows up as the most civically engaged region in her data, the challenge of making complex information accessible, and what it means to be a tech optimist working on projects that generate genuine controversy.Timeline:00:00 Avani Adhikari and GatherGov.02:47 Mapping the development pipeline of America.04:36 Entitlements data as early project signals.05:14 Analyzing 7800+ jurisdictions' public meetings.05:51 Extracting patterns from millions of hours of recordings.06:43 Data centers as a recent fascination.07:31 Nuclear energy as another controversial asset class.08:23 How often these topics show up in council meetings.09:14 Monthly construction spending jumped in 2022.10:16 ChatGPT and data center discourse correlation.11:33 Community opposition to data centers.12:43 Water usage and environmental concerns.16:54 Property value fears and NIMBY dynamics.21:10 Communication gaps between tech companies and residents.25:31 Why transparency matters more than people think.28:27 The tech optimist perspective on controversial projects.31:15 Who benefits from AI infrastructure?34:12 Making complex data accessible through storytelling.37:00 Personal interest in civic tech applications.40:15 The most civically engaged regions in America.43:03 Northeast towns showing up constantly in the data.46:33 Small New England towns and per capita engagement.49:21 Where to follow GatherGov's work.52:24 Newsletter and LinkedIn posts.55:12 Upcoming white paper on data centers.56:01 The commute question.58:04 Living in Tokyo as a teenager.59:35 Hour-long train commute to school.1:00:25 Reading books and buying snacks on the way.1:01:23 Wrapping up.Links:On data centers, from GatherGov.GatherGov homebase.Follow Avani, on LinkedIn.

Tim Wu, author of the Donner Prize-shortlisted book The Age of Extraction, discusses how tech platforms have transformed from tools that enriched the broader economy into monopolistic forces that extract wealth for a few companies. He argues that platform dominance drains economic vitality across sectors, challenges the notion that protecting tech giants is essential for innovation and geopolitical competition with China, and advocates for decentralized innovation.This episode is presented in partnership with the Donner Canadian Foundation.The Hub is Canada's fastest growing independent digital news outlet.Subscribe to The Hub's podcast feed to get all our best content:https://tinyurl.com/3a7zpd7e (Apple)https://tinyurl.com/y8akmfn7 (Spotify)Follow The Hub on X: https://x.com/thehubcanada?lang=enCREDITS:Elia Gross - Producer and EditorSean Speer - HostSean Kilpatrick/The Canadian Press - Photo Credit Hosted on Acast. See acast.com/privacy for more information.

America Out Loud PULSE with Dr. Randall Bock – MAHA explores the tension between government health policy and individual freedom, examining transparency, incentives, and regulatory limits. Competing visions of reform shape debates on vaccines, chronic disease, and public trust, as leaders seek balance between oversight and personal choice in healthcare...

America Out Loud PULSE with Dr. Randall Bock – MAHA explores the tension between government health policy and individual freedom, examining transparency, incentives, and regulatory limits. Competing visions of reform shape debates on vaccines, chronic disease, and public trust, as leaders seek balance between oversight and personal choice in healthcare...

The Challenge of Iranian Nuclear Extraction Guest: Andrea Stricker Andrea Stricker discusses the technical difficulty of removing Iran's 60% enriched uranium. Extracting fissile material and centrifuges is essential to preventing a nuclear breakout, though material remains buried deep within damaged facilities.1900 TOULOUSE

We usually begin the study of physics with a discussion of motion, not because it is easy, or because the modern understanding of motion began with Galileo hundreds of years ago. Rather, Galileo's groundbreaking work provides a paradigm to understand how physics is done today. Extracting out the fundamental essence of motion from all the distractions associated with what turn out to be irrelevant complexities was a monumental intellectual leap for humankind—a leap we often take for granted. Without the leap, for example, Newton could never have made his profound discoveries about the relationship between force and movement, nor his discovery of the Universal Law of Gravitation. But too often we treat these remarkable achievements as something belonging in antiquity.. as if we have moved far beyond them in every way. Nothing could be further from the truth. Applying the very same ideas that Galileo and Newton developed leads us to the cusp of modern physics: the discovery of the dominant mass in the Universe, a vast invisible sea of dark matter. In this episode, we travel over 450 years of physics, from Galileo, to the threshold of our understanding of the cosmos today. Hang onto your hats. I'm also pleased to share a quick PSA. A reminder of our 2026 Origins expedition through the Greek archipelago (July 24 to 31), with a Cyprus add-on (July 17 to 22). If you're interested, it's worth raising your hand early. These trips tend to fill quickly. Express interest at https://originsproject.org/greek-adventure-2026-application/As always, an ad-free video version of this podcast is also available to paid Critical Mass subscribers. Your subscriptions support the non-profit Origins Project Foundation, which produces the podcast. The audio version is available free on the Critical Mass site and on all podcast sites, and the video version will also be available on the Origins Project YouTube. Get full access to Critical Mass at lawrencekrauss.substack.com/subscribe

What if your real estate assistant never slept and handled your entire transaction for you? In this episode of the Real Estate Excellence Podcast, Tracy Hayes sits down with Karan Khanna. Karan is the product lead behind ListedKit AI and the creator of Ava an AI assistant transforming how real estate agents and transaction coordinators manage deals. In this episode he shares how he entered the real estate space from outside the industry and quickly identified inefficiencies in transaction management that could be solved with AI. The conversation dives into how Ava reads contracts in seconds builds timelines automates communication and allows agents to focus on client relationships instead of paperwork. Karan also explains the future of autonomous AI assistants that can manage deals proactively giving agents more freedom and scalability than ever before. If you want to stay ahead in real estate and leverage AI before everyone else catches up this episode is a must listen! Highlights 00:00 - 01:25 Introduction to Ava and AI in real estate Overview of AI assistant Ava Speed of contract processing Impact on agents and transaction coordinators Industry recognition and scale Why this conversation matters 01:25 - 06:05 Karan Khanna background and origin story Coming from outside real estate Product manager mindset Harmony Venture Labs influence Identifying real estate inefficiencies Shift from static software to AI 06:05 - 12:40 Understanding the core problem in transactions Challenges of independent transaction coordinators Managing multiple systems and workflows Agent struggles with admin work Broker expectations for productivity Defining the true industry bottleneck 12:40 - 20:10 Listing workflow and pre contract automation Uploading listing agreements Task creation and deadline tracking Managing offers with Ava AI generated listing descriptions Handling multiple offers efficiently 20:10 - 30:15 Contract to closing automation Extracting contract data instantly Building timelines and calendars Email drafting and communication CRM integration and contact syncing Mobile usage and real time updates 30:15 - 45:10 Scaling agents and teams with AI Template creation for repeat workflows Doubling transaction volume Team collaboration features Improved client communication Reducing manual workload 45:10 – 01:14:25 Future of AI and Agentic Ava Autonomous AI handling deal Inbox monitoring and task execution Text messaging and voice interaction Trust and approval systems Vision for fully automated transactions   Quotes: "Reading four offers at once is now a thing" – Karan Khanna "I just want to be out there selling instead of stuck doing paperwork" – Karan Khanna "We're trying to create an invisible workflow" – Karan Khanna "Ava can wake up and start working on your deals automatically" – Karan Khanna   To contact Karan Khanna, learn more about his business, and make him a part of your network, make sure to follow his Website and LinkedIn.   Connect with Karan Khanna! Website: https://www.listedkit.com LinkedIn: https://www.linkedin.com/in/karkhanna   Connect with me! Website: toprealtorjacksonville.com   Website: toprealtorstaugustine.com    SUBSCRIBE & LEAVE A 5-STAR REVIEW as we discuss real estate excellence with the best of the best.   #RealEstateExcellence #KaranKhanna #RealEstateAI #AIForAgents #TransactionCoordinator #PropTech #AIAutomation #RealEstateTools #AgentProductivity #RealEstateTech #AIAssistant #ListedKit #AvaAI #RealEstateWorkflow #CRMIntegration #AIRevolution #FutureOfWork #AutomationTools #RealEstateBusiness #SalesProductivity #DigitalTransformation #TechInRealEstate

Mistral has been on an absolute tear - with frequent successful model launches it is easy to forget that they raised the largest European AI round in history last year. We were long overdue for a Mistral episode, and we were very fortunate to work with Sophia and Howard to catch up with Pavan (Voxtral lead) and Guillaume (Chief Scientist, Co-founder) on the occasion of this week's Voxtral TTS launch:Mistral can't directly say it, but the benchmarks do imply, that this is basically an open-weights ElevenLabs-level TTS model (Technically, it is a 4B Ministral based multilingual low-latency TTS open weights model that has a 68.4% win rate vs ElevenLabs Flash v2.5). The contributions are not just in the open weights but also in open research: We also spend a decent amount of the pod talking about their architecture that combines auto-regressive generation of semantic speech tokens with flow-matching for acoustic tokens (typically only applied in the Image Generation space, as seen in the Flow Matching NeurIPS workshop from the principal authors that we reference in the pod).You can catch up on the paper here and the full episode is live on youtube!Timestamps00:00 Welcome and Guests00:22 Announcing Voxtral TTS01:41 Architecture and Codec02:53 Understanding vs Generation05:39 Flow Matching for Audio07:27 Real Time Voice Agents13:40 Efficiency and Model Strategy14:53 Voice Agents Vision17:56 Enterprise Deployment and Privacy23:39 Fine Tuning and Personalization25:22 Enterprise Voice Personalization26:09 Long-Form Speech Models26:58 Real-Time Encoder Advances27:45 Scaling Context for TTS28:53 What Makes Small Models30:37 Merging Modalities Tradeoffs33:05 Open Source Mission35:51 Lean and Formal Proofs38:40 Reasoning Transfer and Agents40:25 Next Frontiers in Training42:20 Hiring and AI for Science44:19 Forward Deployed Engineering46:22 Customer Feedback Loop48:29 Wrap Up and ThanksTranscriptswyx: Okay, welcome to Latent Space. We're here in the studio with our gues co-host Vibh u. Welcome. Thanks. Excited for this one as well as Guillaume and Pavan from Mistral. Welcome. Excited to be here.Guillaume: Thank you.swyx: Pavan, you are leading audio research at Mistral and Guillaume, you're Chief Scientist,Announcing Voxtral TTSswyxHost(00:05) Okay. (00:05) Welcome to Lean Space. (00:06) We're here in the studio with trustee co-hosts, Vibhu. (00:09) Welcome.VibhuHost(00:11) Very excited for this one.swyxHost(00:12) As well as Guillaume and Pavan from Mistral. (00:15) Welcome. (00:16) Excited to be here. (00:17) Thank you for having us.(00:18) Pavan, you are leading audio research at Mistral and Guillaume, you're a chief scientist. (00:23) What are we announcing today where we're coordinating this release with you guys?GuillaumeGuest(00:26) Yeah, so we are releasing Voxtral TTS. So it's our first audio model that generates speech. It's not our first audio model. We had a couple of releases before.(00:35) We had one in the summer that was Voxtral, our first audio model, but it was like a transcription model, ASR. Like a few months later, we released some update on top of this, supporting more languages. Also a lot of table stack features for our customers, context biasing, precision, timestamping and transcription. We also have some real-time model that can transcribe not just at the end of the level.(00:56) You don't need to fill your entire audio file, but that can also come in real-time. And here, this is a natural extension in the audio, so basically speech generation. So yeah, so we support nine languages, and this is a pretty small model, 3D model, so very fast, and also state of the art. Performed at the same level as the base model, but it's much more efficient in terms of cost, and also much, in terms of cost, it's also much cheaper, only a fraction of the cost of our competitors.(01:22) And we are also releasing the work that this model is running.swyx What's the decision factor?Guillaume It's a good question.swyxThere will be more. Yeah, Pavan, any sort of research notes to add on?Architecture and CodecPavan: But it's a novel architecture that we develop inhouse.We traded on several internal architectures and ended up with a auto aggressive flow matching architecture. And also have a new in-house neural audio codec. Which, converts this audio into all point by herds latent [00:02:00] tokens, semantic and acoustic tokens. And yeah, that's that's their new part about this model and we're pretty excited that it's, it came out with such good quality and Jim was mentioning. Yeah, it's a three B model. It's based off of the TAL model that we actually released just a few months back and insert trunk and mainly meant for like the TTS stuff, but they need text capabilities are also there. Yeah.swyx: So there's a lot to cover.I always I love any, anything to do with novel encodings and all those things because I think that's obviously I creates a lot of efficiency, but also maybe bugs that sometimes happen. You were previously a Gemini and you worked on post training for language models, and maybe a lot of people will have less experience with audio models just in general compared to pure language.What did you find that you have to revisit from scratch as you joined this trial and started doing this? At leastUnderstanding vs GenerationPavan: when it comes to, for, I think the, there are two buckets, I guess the audio understanding and audio [00:03:00] generation. The audio understanding, like the walkthrough models that Kim was mentioning that we released earlier.The walkthrough chat that we released I think July last year, and the follow up transcription only, models family that we released in January, that would be one bucket, and the generation is another bucket. I think. You can also treat them as a unified set of models, but currently the approaches are a little different between these two.To your question on how audio is fed to the model? In the understanding model, it's very similar to actually Pixar models that we also released,swyx: yes.Pavan: That'sswyx: amazing.Pavan: It was pretty, I, that was the first project I worked on after joined Misra. It was pretty, pretty nice. And Wtu was very similar in spirit.I guess So we feed audio through an audio encoder similar to images through a vision encoder, and it produces continuous embeddings and which are fed as tokens to the main transformer decoded transformer model. Yeah. On the model output is just text. So on the output side, there is nothing that needs to be done in these kinds of mode.I [00:04:00] guess the interesting part of what the generation stuff is, the output now has to produce audio and. The approach that we have is this neural audio codec, which converts audio into these latent tokens. There is a lot of existing attrition and a lot of models which are based off of this kind of approach.And we took a slightly. A different, design decisions around this. But at the end of the day, the neural audio product converts audio into a 12.5 herdz set of latents. And each latent is, has a semantic token and a set of acoustic tokens. And the idea is that you take these discrete tokens and then feed it on the input side.There's several ways to use this at each frame, but we just sum the embedding. So it's like having key different vocabularies. Combine all of them because they all correspond to one audio frame on the input side. The output side is the interesting part on the output side, the, it's not the, I don't know if it's the most popular, but one.Popular technique is to have a depth transformer [00:05:00] because you have K tokens at each time step, like with a text, you just have one token at each time step. So you just do predict the token from the vocabulary with, yeah, with just, you get probabilityswyx: This's a very straightforward text. VeryPavan: straightforward.swyx: Yeah.Pavan: But if you have K tokens, then the name thing would be to predict all of them in paddle. That doesn't work. At least that doesn't work that well because audio has more entropy. And the, one of the techniques people use is this depth transformer where you you almost have a small transformer, or it can be L-S-T-M-R in as well, but people use transformers and you predict the K tokens in auto aggressive fashion in that.So you have two auto reive things going on.Flow Matching for AudioPavan: So the thing we did differently is in, instead of having this auto aggressive K step prediction, we have a flow matching model. Instead of modeling this as a discrete token set we trained the codec to be both discrete and continuous to have this flexibility.So we did try the discrete stuff too, and which it works well, but the continuous stuff works just better. So yeah, we took this flow matching, so the, it's a flow [00:06:00] matching head, which takes the latent from the main transformer and like kind in fusion, it's denoising, but in this flow matching itself, velocity estimate.So you go from this noise t all the way to there. Audio latent, which corresponds to the 80 millisecond audio and then, which is sent through the work order to get back the 80 millisecond audio frame.swyx: Yeah. Is this the first application of flow matching in audio? Because usually I come across this in the image.Pavan: Yeah. Actually, in some sense there are models flow matching models in audio, but I think this specific combination I could be wrong. There could be somewhat. No. I haven't seen. I haven't seen much work in this, so I think it's novel and a lot of it's just a way bigger community, so they, I think they pioneer a lot of these diffusion flow matching work, and it's interesting to adopt some of the ideas there into audio and,swyx: yeah.Pavan: Yeah, I'm, personally that's the think part which is trying out about. One of more meta point is unlike text, even in vision, I think this is true, but in [00:07:00] audio step literature that there is no.Winner model, yet there is no, okay, this is the way you do things. It's it's still by, I think people are still iterating and figuring out like what's the best overall recipe. I guess the idea. Pretty sure there are models which are also completely end-to-end, like NATO audio. NATO audio, but it's still not come to a convergence point where this, the right way to think that.That also makes. A space pretty exciting to explore.Real Time Voice AgentsVibhu: What are some of the ways to look at it?Vibhu: There are ways where you can do diffusion for audio generation, but if you want like real time generation, that's a big thing with the approach I'm assuming that you took. Yeah. And also like how do you go about evaluating different axes of what you care about, yeah,Pavan: good point. I think we so you can do just flow matching diffusion for the whole audio. We didn't even go down that path because one of the main applications is voice agents and we want real time streaming, and that's the use case. That's not the only use case, but that's one of the primary use cases we want to get to.So we [00:08:00] picked the auto aggressive approach for that. And within the auto aggressive space, again, you can do chunk by chunk or you can do so we picked the. I think at least personally prefer the operations, which are the simplest, and so we try to see, can we just add audio as just another head to our regular transformer decode model because that kind of makes it easier for eventual end-to-end modeling of audio text native modeling.Yeah. And it works pretty well. So I guess we went with that and we tried a little bit, but the flow matching head itself, like we had a discreet. Diffusion kind of approach, which also works well, but the flow matching work better.swyx: I was just curious about how you also think about this overall direction of research.Do you basically, when you work with the audio team, do you set some high level parameters and then let them explore whatever, or how does it work between you guys?Guillaume: No I think the way it works is that we are the, we are prioritizing together, I think, what are the most important features because there are many things we can do [00:09:00] in audio.Yeah, I think we try to. These are like how we should do things, for instance. Ultimately what we want to do is to build this through duplex model, but we are not going to start this start there directly, I think is. Some of the project people are doing, butswyx: just to confirm, full effects means it can speak while I'm speaking or,Guillaume: yeah.Okay. Audio. Yeah. Yeah. So intimately we're going to get there, but for us it was, we decided to take it like a step by step. So we start with whatever is the most important. I think support customers, which is the transcription is the most popular use case. Then the speech generation, Soviet time, just a bit before that.And then actually to be like more, but try combining everything all together. But but yeah, we thought it was also important to like separate things and optimize each capability one by one before weswyx: measure of that together. And the super omni model. ButGuillaume: very interesting because as Par said, it's when you work on some other domains of this airline and everything, there are many areas where I think it's not as interesting.For instance. Many places, it's essentially just around data or like creating new environments on a lot of kind [00:10:00] of easy things. But things were, I think the research is maybe not as interesting. Were in audio. There are so many ways to actually build this model. So many ways to go around it. That's the sense I think is really interesting.And what we also tried for speed generation is that we tried multiple approaches. What was interesting that even though they were extremely different, they under the big know the particles but the for matching turned out to be quite more natural. So we are happy with this.swyx: Is there intuition why it maybe like flow matching is just models speech better in some natural fundamental, latent dimension?Pavan: No, I think the main thing is e even at a particular time step, there is a distribution of things.swyx: Yes.Pavan: To be predicted like the way you inflate. So you already know the word that you're speaking and Yeah. The intake space, let's say the word maps register a single token for simplicity.In most cases it does. So there is not a lot of so you just pick the word, but with within audio, even the same word could, even with your own voice, could be inflicted in so many different ways. And I think [00:11:00] any approach which like models this distribution and. And flow matching is one, one of the take.It's not the only one at all, but it's a one which works pretty reasonably well. I think that's better. So you have to pick across several different, the intuition I have is it's, there are some, several different clusters each corresponding to some specific way you would inflict, pronounce that thing.And you can't predict the mean of it because that corresponds to some blurred out speech or something like that. But you have to pick one. And then like sharpswyx: conditional inference.Pavan: Yeah, exactly.swyx: Is that all covered under disfluencies, which is I think the normal term of art. Pauses intonations. By the way, I have to thank Sophia for setting all this up, including like some of these really good notes becausePavan: Yeah.swyx: I'm less familiar with the audios for me.Pavan: No. I think dis dismisses are definitely one such Eno defenses is more likeswyx: which is arms are.Pavan: Yeah, arms. And also repeat like you like,swyx: yeah.Pavan: You do this full of words, your thinking, so you repeat the word.swyx: Okay. Whereas intonation is like a diff, it's up up [00:12:00] speak and all this.Okay.Pavan: Yeah. So I think there is a lot of like entropy. And modeling it as a distribution. And a, any technique which helps with it and the depth transformer is a conditional way of modeling this. And Transformers actually really good at it, even though that's a mini transformers. So I think that worked pretty well too for us too.It's just that the main concentration is when you have a depth transformer. If you have K tokens, you need to do K auto steps, right? Even though it's a small thing, it's K steps, which is very vacant, say heavy, but flow matching. We were able to cut it down significantly. So we are able to do the inference in quad steps or 16 steps and it works pretty well.And there are more normal techniques to bring it down even further to like, in extreme case, one step like we're not doing it yet, but it at least the framework, LEDs itself to more efficient and Yes.swyx: And the image guys have done.Pavan: Yeah.swyx: Incredible work guys. Yeah.Pavan: It now you just. Send a prompt and you get an image.swyx: Yeah. Surprisingly not enough. I think image model labs use those techniques in production. I think it's, I feel like it's a lot of research demos, but [00:13:00] nothing I can use on my phone today.Guillaume: The thing, there's a thing that would be interesting here is that since, indeed I've been so much sure that has been done in the vision community compared to radio dys, stomach, I think there are so many long infra Yeah.And there are so many things we can do to actually improve this further. So it's our first version, but we have so many ways to exist, much better and much more efficient, cost efficient, soswyx: yeah.Guillaume: So really it's not a new field at all, of course, but there are still so many things that can be done.Perfect. It'sswyx: nice. I should also mention for those who are newer to flow matching, I think the creator, this guy's name is Alex, he's done I think in Europe's maybe two Europes as ago. There was, there's a very good workshop. There's one hour on like this matching is I would recommend people look that up.That's the other thing, right?Efficiency and Model Strategyswyx: The efficiency wise, like I, I imagine like the reason is open weights the reason you pick 3.6 B backbone it you are 3.4 B you are, try to fit to some kinda hardware constraints. You kinda fits some kinda basic constraints. What are they?Guillaume: Not necessarily, I think something we care about in our model that they're efficient.So we have a [00:14:00] lot of separate model, for instance. So we have this that is very small, very efficient. We also have a small OCR model that is available. Good, highly efficient as well. And I think on a project maybe there, I think companies are going to take is to have a coverage general model that will do a bit of everything.But that is also going to be expensive. On here. What want say is if you care about this specific use case, if you can actually use this model, it just does that. It's extremely good at it. Survey, very efficient. That's why we can actually add. We do, but also OCR that are like really good at that.And that would be much more cost effective factors and the general model that will contain a lot of capabilities you don't really need. So yeah. So we're doing like general model, but also like more customized model. This,Open Weights and BenchmarksVibhu: how does it compare to other TTS models? It's, we are going follow open wave.We're just dropping it. I think it's pretty good.Pavan: Yeah, I think it's pretty good. Like it, it's definitely one of the best. For sure. It's probably I would say it's the best open source model, butVibhu: decipher themselves.swyx: Yeah.Voice Agents VisionVibhu: Why now? How does it fit into broader ral vision? How do you see voice agents?How do you see voice? I think every year I've heard, okay, you're a [00:15:00] voice. You're a voice. There's a lot of architectural stuff. There's a lot of end time that see it, your solving, but where do you see voice setting?Guillaume: We had so many customers asking for voice. That's also why we wanted to build it.What's interesting in this domain is that. In a sense, if you take something simple like transcription it doesn't seem like something that should be very hard to do for a model. It's essentially, it's pattern recognition. It's classification on this. Models are very good at classifying, right?Or nonetheless, when you talk to them it's not there yet, right? It's not, you don't talk to them the same way you talk to a person. On something, maybe people don't realize it. It's in English it's still much better than in any user language, even compared to French instance. If you talk to this million in French, when you see people talking to this they'll talk very slow.They'll articulate as much as they can. So it's not natural, right? We're not yet to this. And I think, yeah, maybe the next generation will not know this, but yeah, I think people that. But our edge will actually always keep this bias speaking very slowly when they talk to this model. Even if maybe, probably in a couple of years, maybe next year it'll not be necessary anymore.But yeah. But what's interesting is to see that yeah, even for like languages [00:16:00] like yeah, French and Spanish Germans that are not no, no resource on religion. You have a lot of audios there on still it's not as good. And I think a consequence. Because then for this, I suppose just is not as much energy, as much effort that has been put done in some other mod that for some vision or like coding.But but yeah, there's still a lot of progress to be done. I think it's just a question of doing the work and it's clear path I think to get there.Pavan: It's a little fascinating because I worked on Google Assistant I think while back at this point, but it's, I think it's, it like when you take a step back, it's fascinating.It's not that long ago. It was like four years ago or five years ago, and it's now it's completely audio in, audio out and the function calling and the whole thing happens completely end to end. And in a very natural,swyx: yeah,Pavan: natural way and still ways to go. Kim was telling, even despite all the previous, it's not like you're speaking to a person.When you talk to any of these agents, bots, or voice mode kind of situation, it's still like a gap. I think that's the great part and I feel like with even the existing [00:17:00] stack, we should be able to get to this very natural speech conversational abilities soon enough I guess.And we'll also hope. I get thatGuillaume: on this kind of the next step, right? Because when you talk to these agents, like usually people are just writing to them and sometimes they'll this very clear, for instance, you are, you want to write code, but you are, you have a very clear idea of how you want the model to implement what you in mind.But so here you are able to spend a lot of time writing. So it's not really efficient on audio is really like a natural interface that is just not there yet, but I think it's just gonna be the place.Vibhu: How's it like building, serving, inferencing, like we see a lot about, it's very easy to take LMS off the shelf, serve them.Fine tuning, deploying. I know you guys have a whole you have Ford, you have a whole stack of customizing, deploying. Is there a lag in getting that. Like distribution channel. Are you helping? There is. So like prompting, lms, you can have them be concise, verbose, all that.They're built on LM backbones, these models. How do you see all that?Enterprise Deployment and PrivacyGuillaume: Yeah, I think this is a lot of what we're doing with our own customers. Very [00:18:00] often they come to us, so it's for different reasons. I think one reason is sometimes they have this lot of privacy concerns.They have this data that it's very sensitive. They don't want data to leave. The companies, they wanted to stay. Inside the company. So we have them deploy model in-house. So either on a, either on premise or on private cloud. So they're not worried that it's given to a third party on the there some leakage.Sometimes they have this kind of many companies have this different, sensitivity of data they have like sometimes channel chat can send it to the cloud has to stay there. So then it creates some kind of heterogeneous workflows where it's annoying. You cannot send some data to the cloud.This one you can, so here, when we actually deploy the model for them, they don't have this consideration. They are like not worried that, this is going to leak. Everything is much easier. So we help them basically do this on the, so it's one of the very proposition. But but the other is very often, when customers use this off the shelf close model, but very sad is that they are not leveraging, these data that have been collecting for four years or something for decades.So much data. Sometimes it's trillions of tokens of [00:19:00] data in a very specific domain. Their domain, which is data that you'll not find in the public, on the public internet. So data on which, like close model, we actually not have access to one, which that's going to be really good. So if they're using like closed source models are basically not benefiting from all these insights.All these data they have collected three years, they can always give it into the context that in France, but is never as good as if you actually train the modern analysis. So yes, that's basically what we help them to do. We actually provide them some purchase, basically what we announced at GTC this week.So we provide them with this, it's basically like a platform with a lot of tools to actually help them process data. Trained on that. Yeah, it's actually the same thing that we're using in the science team. So it's actually very better tested infrastructure, like a lot of efficient training cut base.For a quality pre-training like a fine tuning, even doing S-F-T-I-L. So we help them do this using the same tools as what our science team is building is using. So since it's tools that we've been using for two years now, it's really better tested. It's really sophisticated.So it's the same thing. We are giving to them, giving the company the same thing [00:20:00] that what are same still using internally actually build their own ai and it makes a really big difference. I think sometimes customers. And many in general don't realize how much better the model becomes when you fine tune it on your own data.And you can have a, your model is here. You start from there. You have a cross source model, which is sort here, but if you actually fine tune it can actually really go much further than this. And then you have a very big advantage. The model is trained on your entire company knowledge, so it knows everything.You don't have to feed like 10 K tokens of contact at every query. So it's it's much easier. It's a bit, I think using a closed source model is really sad because it basically puts. You are not leveraging all this data and you are going to be using the same model as all your old competitors when you're actually using, everything you have been collected for years, which is really valuable.So yeah. So we help basically customers do this. We have a lot of solution I mean deployed for engineers that go in the company that basically look at the problem customers are facing to look at what they're struggling to do what we should do to solve it. So we help them solve them together.So it's I think our approach is a bit different, but here. [00:21:00] Some of their companies and competitors, it's, we don't just release an endpoint on sale, do some stuff on top of that, or we don't just give a checkpoint. We really look very closely with customers. We look at the issues they have, we had them solve them.We really make some tailored solution for the client are facing. Some example are also going to be, sometime we have some customers. They really wanted to have a really good model, really performance on some, like Asian languages on the, if you take some of the shelf models, they can speak it, they can write in this language, but it's not amazing.This language would be like maybe zero 1% of the mixture. So it has been included during training, but very little. So what we did here is upgrade. We trained a new model for them, but so this language was 50% of the mix, so it's much, much stronger. It knows of the dialects, it knows the, so it's yeah.So it's some example of things we can do and it's really arbitrary, custom. I think you had some of their customers, for instance, they wanted some. They wanted some 3D model that can do audio with a very good function cable. So something you wanted to put in the car in particular, they wanted this to be offline because in a car you don't necessarily have access to internet.So [00:22:00] yeah. So here we can actually build the solutions. There is no like model out of the box on this. In the internet you have this very, you have this very general model generalist, like he's strong model. But for things like this, they always want at specific solutions and on some other reasons.Sometimes they come to us is because, like they, they experiment with some closed source model. They get some prototype. They're happy with what they build. They, it works well. They're happy with the performance, and then they want to go to production and then they analyze. But it's extremely expensive.You cannot push this. It's so then they come back to us on this. They can help us build the same thing as this, but using something much cheaper on here. And here we can sometime be something 10 x cheaper by just functioning a model and it'll be better OnPrem on their old server and also much cheaper as well.So yeah,swyx: that's the drop pitch right there. Take all themoney.Vibhu: And outside of that you do, we do put open wave models so people can do this themselves. I feel like not enough people go outta their way.swyx: They're not going to, they're gonna ask them to do it as the expert. IGuillaume: think initially we didn't know, [00:23:00] we wanted completely short at the beginning of the company because, I think our study was not exactly the same as what it is today, but what we underestimated initially is the complexity of deploying this model and connecting them to everything to be sure it has access to the company knowledge on the, and it was, yeah, on, we were seeing customers struggling with this, but it was even, that was three years ago and no, things are much more complicated because now you don't just have, text on SFT on a simple instruction following.You have reasoning like your agents, you have like tools. You have a multimodal audio, so it's much more complicated than before. And even back then it was hard for customers. So they really need, have some support and this is why actually providing like always some four D position as well. The processFine Tuning and Personalizationswyx: I'm curious is there also voice fine tuning that people do?Pavan: So in this forge we also have a say unified framework. And the hope is like the er speech to text that we released earlier this year. And even the ER chart that we released last year. And I think a big people, I think there's a big, rich ecosystem [00:24:00] of people fine tuning whisper, and people want the same thing with w so it's much stronger than Whisper.And yeah, the the platform offers that kind of fine tuning yeah, which could be any kind of fine tuning. Like for instance, even sometimes people want to support new languages to this, which are tail languages, which we hope to cover. Certain natively, but if there is a language where you data and you want to frank you, I think this is a good use case.Or the other use cases, you, it's the same language, like even English but it's in a very domain specific way.swyx: Yeah. Terminology, jargon, medical stuff.Pavan: Exactly. And also there's specific acoustic conditions like there's a lot of noise or the, and. The model will do decently in most conditions, but you can always make it better.And that those are some of the use cases where you can improve it e even further. And that's one good use case for this and for text to speech. We're just releasing it so we'll have support for that soon too. I think it's similar use case.Voice Personalization Pavan: It's little different the kind of things that you want to extend a [00:25:00] text to speech model to, which could be like voice personalization, voice adaptation for enterprises.Many enterprises need very specific kind of tone, very specific kind of like personality for this kind of voice. And all of those are like good use cases for fine tuning.swyx: This one I was gonna ask you, we never talked about cloning voice clothing here. How important is it, right?Like I can clone a famous person's voice. Okay. ButPavan: the main use case would be like for enterprise personalization, like enterprises need like a lot of customization. You don't want the same. Voice for all the enterprises. Each enterprise want a customized, specialized something which is representative both their brand and also their, I guess safety considerations and the use case I think the kind of thing that you would deploy as a empathetic assistant in the context of a healthcare domain would be very different from the kind of thing that would be in a customer support bot and would be different from like more conversational aspects.I think those are the. [00:26:00] Customizations you would expect from enterprise. And that's the main use case, at least from our side.Vibhu: My, my basic example is you don't want to call to customer services and have the same exact voice. It's just, it's gonna be weird.Long-Form Speech ModelsLong-Form Speech ModelsVibhu: But also on the technical side of this, so there's like a few things in TRO that I thought were pretty interesting.He's a big fan of this paper. Oh, he said very good paper. He said this is the best SR paper he's ever read. Yeah. I've hyped up this voice paper enough. We covered it. Somewhere, but a big thing. So Whisper is known for 32nd generation a 32nd processing. You extended this to 40 minutes. There was a lot of good detail in the paper about how this was done.Even little niches of how the padding is. So it's very much needed. You need to have that padding in there, the synthetic data generation around this. I'm wondering if you can share the same about the new speech to text, right? Text to speech. So how do you. How do you generate long form, coherent?How do you generate, how do you do that? And then any gems? Is there gonna be a paper?Pavan: Yeah. Yeah. They would be a technical report. Okay. Yeah. I think I could have a lot of details.Real-Time Encoder AdvancesPavan: But me I think the [00:27:00] summary of it, actually, some of the considerations in this paper were, because we started with the wipa encoder as the starting point, and now we have in-house encoders, like the bigger time model, for instance, which we released in January.Also release a technical report for that real time model as well, which is this dual stream architecture. It's an interesting architecture. You should check it out. And there we have a causal encoder and I don't think there's any strong, multilingual causal encoder out in the community. So we thought it's a good contribution.So that's one nice encoder there. Other people want to adapt. That's a good end code. And we train it from scratch. I think her. Post stack is now mature enough that we are able to train super strong ENC codes. And some of these considerations, like spatting and stuff, is a function of the Whisper ENC code.And now that we train encoders, inhouse the design concentrations are different.Scaling Context for TTSPavan: And for the question on text to speech, I think that's also leans onto the original auto aggressive decoder backbone. I think, it says very, almost identical considerations. I think the long context in it's not even long con, [00:28:00] so the model processes audio at 12.5 herds, so one second maps to like 12.5 tokens.So I think one minute is like 7.8 tokens. You can get like up to 10 minutes in eight K context window and get half an hour and 30 K context window. So that's and 30 2K context is something that's we are very comfortable training on. We can extend it even much longer. 1 48 K. Okay. You can naturally see how it can extend to even our long generations.Yeah. We need the. Like data recipe and the whole algorithm to work coherently enough through such long context. But the techniques are some way very similar to the text, long context modeling. And the key differences, it's just doing flow matching order regressively instead of a text open prediction.swyx: Okay. I think that was most, most of the sort of voice questions that we had. ButWhat Makes a Model SmallVibhu: I have a big question on Mr. Al, Mr. Small. So what is small? How do we define [00:29:00] small? What is this? What is this? I remember the days of Misal seven B on my laptop. The snuff fitting on my laptop. I could run it on the big laptop, butGuillaume: it's just additional.Question of terminology, like here what we did, baseball is north active parameters, but it's true. Really not give it another name, but yeah, we could have called it medium, but only, I,I suppose it's a model that we released mixture of experts. It's a model that combines different model before which we were doing the same, is that we had one model, general model for Israel. Doing instruction following, were like a separate model that was Devrel trial. So qu coding specify specific to code with another model for Reason Maal.So this were separate artifacts built by different team at trial on what we're doing is basically merging all of this. It was, you had pixel trial was the first vision model. We was like a separate model on the way we do things internally is that we have one team focus on one capability, build one model.On the means mature, mature enough, we decide to merge this into the [00:30:00] matrix. But here it was the first time we basically match all of this into one. But there are some other things we did at first time to merge time, for instance, like more capabilities or function coding I think would be, are, it's going to be much, much better in this trial, small platform.But but yeah, so it's our latest model on the working is,Vibhu: and yeah, key things is it's very sparse. Six, be active pretty efficient to serve. 2 56 K context. Yeah,Merging Capabilities vs Specialistsswyx: I think what's interesting is just this general theory of developing individual capabilities in different teams and then merging them.Where is this going gonna end up?Vibhu: Like we've seen the five things put together in this. Yeah. What are the next five teams?swyx: I think actually OpenAI has gone away from the original four Oh. Vision of the Omni model. This was what they were selling. All modalities and all modalities out.But I feel like you might do it.Guillaume: I think there's some mod where it's not competitive use, for instance for audio. For audio here, if you want to do transcription, I think it makes no sense to use a model. If you just want to trans tech it, it'll be very inefficient. If you want to do audio, you probably just want to be the [00:31:00] one VR 3D model performance essentiallyswyx: the same.It's going to be incredibly cheaper. So here, that's why we wantGuillaume: to have a separate but just does this. Yeah, I think the question is just, yeah. If you are to, to your model. By speech and you asking like a very complex questions on how you do this on the, just to cascade things. Do you want to put a d in a model that has like a one key around it?It's like a, not a competitive discussion, I think unaware if you doing into the direction, but that's possible. Of course. But yeah. But I think for us, the next capabilities we want to try to integrate into these models when we are going to be yes, like marketing or no reasoning better, I think more capabilities that people don't talk too much about, but at high bottom, I think for our customers in our, on different industries, for instance, things are around like a legal computer.I design all these things that is this males out of the box are to put at that. Because people, if you don't prioritize this, there is not like too benchmark on that. Butswyx: this done how toGuillaume: make this good and this just start to do the work. Extracting some that processing it [00:32:00] expression. So yeah.But we are offering the imagine to this.swyx: I think for voice. Yeah. The key thing I think over maybe like the last year or so with VO and gr Imagine and all these things is joining voice with video, right? Which people don't understand spatial audio because like most TTS is just oh, I'm speaking to a microphone in perfect studio quality.But when you have video, like the voice moves around.Pavan: That's true. The constitution was a little different in the sense that there it's like a a standalone artifact where you get the whole thing and you consume it. But in a conversational setting, it's a, you need the extreme low latency.swyx: Yeah,Pavan: streaming would be one of the primary concentrations.swyx: You can build a giant company just doing that, right? So you don't need to do the voice, but I was just know on the theme of merging modalities, that is something I, I am like, wow. Like I didn't, everyone up till, let's say mid last year was just doing these like pipelines of okay, we'll stitch a TTS model with a voice thing and a lip sync [00:33:00] thing and what have you.Nope. Just giant model. Yeah.Open Source MissionVibhu: I have a two part question. So one is, it's still open. It seems like open source is still very core to what you guys do and I just have to plug your paper. Jan 2024. This is the one trial of experts like. Very fundamental research on how to do good.Moes paper comes out very good paper for anyone. That's just side tangent. No.swyx: This thing caused, we bring back, eight by 22 was like the nuclear bomb for open source. I think it takes Shouldn be more seven B more. Yeah. Yeah. But this is a bigger opposite than me.Yeah. Yeah I don't remember this. I remember, I don't think it was January, right? It was like new reps it was, it dropped during new reps and everyone in Europes was December of 25th, I think. Yeah. The model was did as well.Vibhu: It's just a little update probably.swyx: Yeah. No, but you have a point to make.Vibhu: No, you gotta check that. But then, I just want to hear more broadly on open source for you guys, and when you had asked earlier [00:34:00] about what's next, what are the other, side tapes working on you. You put out Lean straw. This,swyx: it's not necessarily surprise. I was like, I don't, this doesn't fit my mental model or Misra.Guillaume: Yeah. First for open source in general, I think it's really something which looks to the January of the company. I think we started it per once, is we so we have open sourcing with, since the beginning and even before this. So before this, so me and Tim were at Meta, we released LA and I think what was really nice.To see that before this, for most researchers like universities, it was impossible to work on elements. There was no alien outside. And if you look at many of the techniques that were developed after, for instance, was open source all this post-training approaches like even DPOD, like preference optimization, all of this were done by people that had access to this portal.And it'll have been impossible to do without this. So it's really making sense, move faster. So we really want to contribute to this ecosystem. I think like the deep and also like very lot of impact. All these papers that are I think in the open source community are really helping the science community as a whole to move faster.So [00:35:00] we want contribute to this ecosystem. That's why we're releasing very detailed technical reports. So ma trial and our first reason model, and ation, lot of results, things that work, things that did not work as well. Think helpful on the, yeah, so for the audio model also to share a lot of details, share of them for real time model.And the, yeah, so we really want to continue this, basically belong to this community of people who share science. I think we really don't want to be, leading in a world where the smartest model, the best models are only behind, close doors. Only accessible to a shoe companies that we, as a power to decide we can use them on it.I think it's a scary future. We don't want to live in, we really want this model to be accessible to anyone that want. Intelligence to be used unaccessible by anyone who can use it. So yeah, so that's why we are pushing this mission and source model. Yeah. So not, so yeah, no strategy. So it's open source, not the first model, so not the best on the Yeah.Lean and Formal ProofsGuillaume: LIN trial I think is also one step into this direction. So it's yeah, a bit different than what we are usually releasing. But we have a small team internally [00:36:00] working on them. Formal proofing, formal math. So I think a subject we care about in general and we were working on reasoning. I think we started too early before doing reasoning without LMD is very hard, especially when you work with formal systems because the amount of data you have is negligible.It's addressable community of people writing like formal proofs. But the reason why we like it is because I think there is if you look at what people are doing with reasoning, is there, the problems that you can use. Are usually going to be problems where you can verify the output. So for instance, all this ai ME problem where the solution is a number between 100, like a thousand.So you can verify, compare this with a reference or it's an expression. You can actually compare the output expression generic with the reference. But there are many, most of them have problem and most of the reason problem. There is no like way to easily verify the solution. If the question is show that F is continuous, cannot compare in the reference, right?If it's a probe that this is true or probes is properties, there is no way to. You cannot act, simply verify the correctness of your proof. So it's hard to apply the, there is no referable reward here. So [00:37:00] what you could provide is of course, like a judge and judge that will look at your proof. But it's very hard and it's very, you could do certain, some reward hacking happening there.So it's difficult. You could provide like a reference proof, but then there are also many ways to prove the same thing. So if the model says give negative reward because it's a different poop, maybe it was still digit proof, just different. So it's not going to work well. What's nice with lean and with formal probing is that you don't have to worry about this whatsoever.We just,swyx: they're all function is largely compiles in lean is functionally the same. Exactly.Guillaume: It's like a problem if it compiles it's correct. It's very easy. And you can apply this and then you can,swyx: it's just way too small. So no human will actually go and do it.Guillaume: Yeah, that's exactly.It's the only people can do it. It's like a very small committee of people doing a PhD on that. So it's super small. And it's sad because it's actually very useful on not just mat, but also in software verification. So for instance, software verification today. So tiny market. Very few industries work on this and we need that.It's usually going to be like companies like building airplanes, air robotics,swyx: likeGuillaume: things [00:38:00] where they absolutely want to be sure. Life depend on this, but it's very rare that people formally verify the correctness of their software. But I think one of the reasons for this is simply that it's just hard to do.swyx: Are you think of TLA plus? It's the language that some people do for software verification? No. That people use in a ference, but but yeah, it's the reason I think why people don't use it more and why this industry is not as big as could be is because it's very hard. But now with cutting edges that are there, it's going to be very different.Guillaume: We're going to see much more of this. So I think yes, industry there is going to be much larger in the future that we, these models. So yeah. Here also anticipating this a little bit, we wanted to work on that because it's proving like a math theory and like a, essentially the same tools.swyx: Yeah.Reasoning Transfer and Agentsswyx: One of my theories is that because the proofs takes so long, it's actually just a proxy for long horizon reasoning and coherence and planning. Maybe a lot of people will say okay, it's for people who like math. It's for being okay. It's like a niche math language. Who cares? But actually, and you use this as part of your data mixture for [00:39:00] post-training and reasoning, actually, it might spike everywhere else.Yeah. And I think that's un under explored or no one's like really put out a definitive paper on how this generalizes.Guillaume: Yeah, absolutely. AndPavan: I think evenGuillaume: that's what we're seeing already. For instance, you should do some reasoning on math as then the American should do reason even.Yeah. In the early stage. So we, the, there is some transfer, some sort of emergence that happens. And I think some, it's also interesting, it's not just I think the topic in general, but it's, there is a lot of connection with this on including agents because. Sometimes the model can see like a three that it has to prove it's very complex, but then it can take the initiative to say, I'm going to prove this three lr.I'm going to suggest three Rs, and I'm going to in parallel prove each R. So three of them in parallel with sub agents, but I'm also going to prove them in theory and the three tool so you can do this also. Pretty interesting. You can, even if you fail to put one of the LeMar, you can actually, maybe you succeed to put the normal lema too, so you get some possible reward here.So it's a bit less Spartan issue, just get to zero one for the entire thing. [00:40:00] So it's pretty interesting. I think we can actually,Vibhu: yeah, it's also an interesting case just for specialized models in general, right? Like the cost thing you show is pretty interesting yeah, similar score wise, you are, thirty, seventy, a hundred fifty, three hundred bucks.Smaller.swyx: I think cost is a bit unfair, right? ‘cause this one is at like inference cost. It's always there on top with their margins on top of it. But, we don't know anything else, so we gotta figure it out.Vibhu: Okay.Next Frontiers in TrainingVibhu: I did wanna actually push on that more. Not on cost, but you mentioned about, okay, it's a great way to have verifiable long context reasoning.What are other frontiers that, I'm sure you guys are working on internally, there's a lot of push of people pushing back on pre-training. Scaling, RL pushing, compute towards having more than half of your training budget. All on rl. Where are you guys seeing the frontier of research in that?Guillaume: You mean theVibhu: just in foundation model training in the next, one thing that you guys do actually is you do fundamental research from the ground up, right? So you probably have a really good look at where you can [00:41:00] forecast this out.Guillaume: Yeah. I think for us we're still working a lot on the pre-training side.I think we are very far from situational, the pre-training. I think ML four preprinting will be like big step compared to everything we have done before. So we are pretty excited about this. And I think on the other side, I think now we have more and more to think about this algorithm that will actually support this very long trajectories.I think when it was, for instance, GRPO for it doesn't really work this any bit of policy. Which was okay initially because you are solving math problem that can be solved in like a few thousand tokens. So the model can alize them pretty quickly. So when you do your update, the model is never too far off.It's never too far off. But now when you are moving towards this kind of problems where certain takes hours, like six hours to get a reward, then your model is co pick places. So you have bi new infrastructure that supports this, but also new A, so now everything we're doing internally, we're trying to. Build some infra that we actually anticipate is what we have in six months, one now, which is this extremely no scenarios on the, I think when we started Missal, part of me and [00:42:00] we wanted to, is very nice under element where people are there, they can do research, they like with a lot of resources.So it was nice. I think things changed a lot when I think when J Pity came out. I think after that I think was. This one is same again. But but yeah, but it was nice. And I think we also want to work part of this descrip beforeswyx: coming to the end.Hiring and Team Footprintswyx: We're just, obviously, I think you guys are doing incredible work.You've, they are a very impressive vision for open source and for voice. What are you hiring for? What's the what are you looking for that you are trying to join the company?Guillaume: Yeah, so we are hiring a lot of people in our sense team. We're hiring, in all our offices. So we have a, our H two is in France in Paris.We have a small team in London. We like a team in Pato as well. Co we open some offices in in SAU, in Poland. So one in Zurich. We also like some presence in New York as well on Sooner one in San Francisco. So we all bit either way also like hiring remotely. So we're going the team trying to hire like very strong people.I think we want to stay, so the team is not. Instead of fairly small team. [00:43:00] But I think we want to keep it that way. ‘Cause we we find it quite efficient. So like a small team they agile so yeah.swyx: Okay.AI for Science Partnershipsswyx: Let's focus on science and the forward deployed. We actually are strong believers in science.We started the our new science pod that focuses specifically on the air for science. What areas do you think are the most promis.Guillaume: What we're pretty excited about right now, and something we have already started doing or that we'd probably be able to share more about this in a couple of months, is that we are exploring AI for science.And there are a lot of areas where we think that you could get some extremely promising buzz. If you were to apply AI in these domains. There are a lot of long inputs. You just have to find these domains where actually AI has not been yet applied, and it's usually hard to do because the people working in those domains don't necessarily know the capability of these models.They don't know. How I would just have to pair them with Yeah, exactly. Your researcher slashing, which is actually hard to do. But this matching, we're doing it naturally with our customers. So we have some company we are very closely with. So for instance, ISM Andreesen are one of our partners, so we're doing some research with them on their other, like tons of extremely interesting problems.Columns in physics, in [00:44:00] science matter science that they're essentially the only ones to work on. ‘cause they're doing something No, no one else is doing on the, yeah. So there are many domains where AI can actually revolutionize things. Just you have to think about it on you familiar with what can do or to apply it.So yeah, it's something where more modeling with our partners, with our customers sort AI for s, but.swyx: Yeah. Okay.Forward Deployed Skillsswyx: And then for deployed what it makes a good four deployed engineer, what do they need? Where do people fail?Guillaume: I think it's usually you need people that are very familiar with the tech and not necessarily with a lot of research expertise, but that are actually pretty good at using this model that can actually like that know how to do functioning, that know how to like, start some error pipeline.And it's it's not easy. It's something that mucus. Majority of companies will not be able to do this on their own. So here I think we need people that are, that like to solve problems that are accept solving some complex, very concrete problem. It's applied science basically.And yeah, so I think it's not too different. I think from the case you need in research because it's essentially you are trying to find solutions to problems that in [00:45:00] customers have not yet. So sometimes it's easy. Sometimes you're here to do the work. You have to like create synthetic data.Find some edge case. So it can be, yeah. Depends on the problem. But but yeah, you have to, I think it also a bit of patience on the be creative. I think very similar skill is Asian,Pavan: the diversity of the work they do. It always surprises me. It's it's, it goes all the way from the kind of stuff they encounter in industries.It's just very interesting. I think.swyx: Any fun like success anecdotes.Guillaume: Yeah, it can be actually training this small model on edge that just we do one specific thing can be like training some very large model without some specific languages as well. Making models really good at some tube use, like for instance, computer ID design, these kind of things.Is that pairing with vision as well? Yeah,Pavan: and the fact detection for chips or like in, in factories identifying things like it, the. Diversity could be anything where you can deploy these foundation models. So yeah the work to make it work in that specific setting, basically whatever it takes to make it like add value in that, by the way, workflow.Vibhu: Yeah. [00:46:00] And it goes across the stack, right? Like even just pulling up the website like.swyx: It's so broad on compute. It is so broad.Vibhu: We didn't even touch on if you have a coding CLI tool. One thing you guys were actually like, I think the first tool was agents, ral agents. You had the agent builder, you can serve it via API and all that.And I'm guessing forward deploy people.Guillaume: Yeah.Vibhu: Help build that out and stuff.Customer Feedback LoopGuillaume: It is also why we are, so we're doing many things, but I think that's also part of the value proposition that sometime know customers. They're always very. Extremely careful about their data and they don't want to, they don't like, trusting so many partners, trusting one partner for code, giving the data to another third party for like audios and another one.So they don't like this here. What they really like with our approach that we can help them on anything so they don't have to send the data to so many clouds. So yeah,swyx: I think that there can be many orders of magnitude more. F Ds then research scientists and they don't need your full experience, but they're still super variable to customersGuillaume: in practice.These two teams [00:47:00] are still quite intertwine, very often. Yeah. So first of all, they're using the same tools, the same data pipeline and everything on the, it's it's very helpful for the science team to get the feedback and the solution team ‘cause they can. Look at these customers are trying to do this.This is not working. It can really be show in the next version. Yeah. But this is basically a real world eval. Yeah, it's real world eval and it's not something, for instance, if you're just working in the lab, it's just ships model. But you don't do this work of for customers. You have no idea for whether your model is good at this H case.For instance, you even in year found this, right? So yeah, there is a very gap, big gap between the public benchmarks that are very like academic. OnPavan: the rare cases are just very diverse and in the specific concept of a customer, you can fine tune and make it like first evaluate, create a solid eval, benchmark, and then measure in the context of their, the kind of audio.Like for instance, one use case is literally just, there's the word for kids and they have to just say it out. It's a very specific thing. You're just saying one word and then you have to you, you'll grade the kid whether they did it right or not. It's [00:48:00] like R for, but so there're very diverse use cases and the idea is that they, the.Applied scientist engineer will go and make it better. And then from the learnings we incorporate it into the base model itself. So it's it's just better out of the box.Vibhu: Yeah. It's a good full circle system. Like the foundation model evals are all just proxies of what you really, you're never gonna have one that says it, it doesn't make sense for there to be, a one word transcription like that.It's not something you wanna fit on. Perfect.Wrap Up and Thanksswyx: Everyone should go check out everything that Michelle has to offer and try the TTS model, which will link in the show notes. But thank you so much for coming tha thanks. Such a stretch. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

With the current AI boom, physicians are looking for ways to use this new technology in their practices. Dr. Edmund Tsui interviews Dr. Sophia Y. Wang on her Ophthalmology Science article, "Leveraging Large Language Models with Sequential Prompting to Extract Eye Examination Findings from Free-Text Ophthalmology Notes," which showed that LLMs are capable of efficient, automated, and accurate extraction of relevant information from ophthalmology free-text progress notes. Leveraging Large Language Models with Sequential Prompting to Extract Eye Examination Findings from Free-Text Ophthalmology Notes. Ruan, Franklin Y. et al. Ophthalmology Science, Volume 6, Issue 1. If you're working on research right now, consider sharing your latest research at AAO 2026. Abstract submissions for papers, posters, and videos are open now, through April 14. It's a great opportunity to present your work — and connect with colleagues from around the world. Start your submission today at aao.org/pod26.

The war in Iran is not a shocking event in the sense that we knew it was coming. Trump has been not so much hinting, but sounding a fog horn for months. As we've been covering on this show, he's been amassing military assets in the region since the end of January. So not surprising. What has surprised is Iran hitting their neighbours, the Gulf states, so hard, including oil fields and refineries. This has surprised markets. Hence what we're seeing in Asia and at home. Same goes for the price of oil. Yesterday when we talked about that Goldman Sacks estimate of $100 a barrel by the end of the week. We got there by the end of Monday. Remember they also warned of $150 a barrel by the end of the month? Let's hope we're not there by Friday. Oil and fertilizer run our agriculture sector. I listened to a grain farmer in Christchurch yesterday say daily harvesting costs would increase by $2000-$3000. Already. We are not immune to the price spikes, but we're also not very well insulated from them either. We're reliant on energy imports to keep us afloat now more than ever. Marsden Point used to refine 70% of our petrol and 90% of our diesel. Extracting oil and gas became a cardinal sin under Labour. You don't know what you've got ‘till it's gone. The international shocks, which as we all know, aren't now so shocking, are also driving a surge in support for economic nationalism and self-reliance. Think New Zealand First. To this Government's credit, it has extended our emergency oil back-up supplies and done deals with the Singapores of the world so we don't run out. As a back stop, we're about to do LNG. But again, that's imported and subject to shocks. The more global events we have, the more we yearn for self-reliance and greater energy independence. Our world-beating exports keep us afloat, but what use are they without a reliable supply of reasonably priced inputs that help produce them?See omnystudio.com/listener for privacy information.

While some models aim to explain qualitative features of brain activity, other aim to reproduce experimental data quantitatively. If so, model parameters must be adjusted to make the model predictions fit the experimental data. A complication is that in most neurobiological applications, there is not a unique best fit: many parameter combinations give equally good model fits. Recently, the guest, together with colleagues, made the tool AutoMIND to fit spiking network models to data.  

Quick Howto: Extract URLs from RTF files https://isc.sans.edu/diary/Quick%20Howto%3A%20Extract%20URLs%20from%20RTF%20files/32692 German Agencies Warn of Signal Phishing Targeting Politicians, Military, Journalists German: https://thehackernews.com/2026/02/german-agencies-warn-of-signal-phishing.html English: https://www.verfassungsschutz.de/SharedDocs/publikationen/DE/praevention_wirtschafts-und_wissenschaftsschutz/2026-02-06-gemeinsame-warnmitteilung-phishing.pdf?__blob=publicationFile&v=3 Someone Knows Bash Far Too Well, And We Love It - Pre-Auth RCEs https://labs.watchtowr.com/someone-knows-bash-far-too-well-and-we-love-it-ivanti-epmm-pre-auth-rces-cve-2026-1281-cve-2026-1340/ Pre-Auth RCE in BeyondTrust Remote Support & PRA CVE-2026-1731 https://www.hacktron.ai/blog/cve-2026-1731-beyondtrust-remote-support-rce https://www.beyondtrust.com/trust-center/security-advisories/bt26-02 Fortinet FortiClientEMS SQLi in the administrative interface https://fortiguard.fortinet.com/psirt/FG-IR-25-1142

This episode explores the laws of extracting juice from fruits on Shabbos and when liquids that emerge are permitted or prohibited. We discuss olives and grapes crushed before Shabbos, when their juice is automatically allowed, and how nullification works when juice mixes immediately with existing liquid. The shiur clarifies key distinctions between juice that appears independently versus juice that blends right away, and why that difference matters.We also cover practical scenarios: placing ice or snow into drinks, soaking raisins or grape remnants to create a beverage, filtering liquids prepared before Shabbos, and squeezing fruit directly onto food versus into an empty dish. Special attention is given to unripe or inedible fruits, differing rabbinic opinions, and when stringency is recommended. The episode concludes with everyday applications such as squeezing lemon onto food and how intent and timing affect permissibility.

We share a simple image—a half-full cup held at arm's length—to show how small problems grow heavy when we grip them too long. Using the acronym P.R.O.B.L.E.M.S., we reframe stress into strategy, turn setbacks into lessons, and practice letting go instead of wearing out.• Problems as predictors of success based on response• Life is difficult as progressive overload for the mind• Reframing problems as opportunities to act• Noticing blessings that often arrive in disguise• Extracting lessons to grow, not just survive• Accepting problems are everywhere and relative• Reading the messages inside setbacks• Believing everything is solvable with time and helpIf you need anything or have any thoughts or comments, just text me or let me knowhttps://aarondegler.com/

In this insightful episode, I chat with Diane Strand, founder of JDS Production and co-founder of the nonprofit JDS Creative Academy. Diane shares her journey from working in Hollywood on popular shows like 'Friends' to focusing on empowering mainstream, foster, at-risk youth, and special needs adults through the visual, performing, and digital arts. She discusses the significance of the arts in life and business, and how creativity can drive success, community building, and personal fulfillment. Learn about her mission to nurture creativity across the globe, her approach to education, and the inspirational projects she has lined up for 2026, including a humanitarian tour in Kenya. Connect with Diane to explore more about how the arts can impact life and business.00:56 The Importance of Arts in Life and Business03:41 Teaching and Learning Through Arts05:44 Extracting and Nurturing Creativity08:25 Personal Journey and Reflections14:35 Building a Nonprofit and Giving Back21:22 Future Plans and VisionConnect with Diane: • https://www.linkedin.com/in/dianestrand/Connect with Raul: • Work with Raul: https://dogoodwork.io • Free Growth Resources: https://dogoodwork.io/resources• Connect with Raul on LinkedIn (DMs open): https://www.linkedin.com/in/dogoodwork/ 

It's largely invisible to most of us, but without the industries and workers that drill or mine the world's raw materials, and the arteries of transport that bring petroleum and other resources across the seas, global capitalism would sputter to a halt. Scholar and writer Laleh Khalili has traveled on the system's massive container ships and describes in unflinching terms the inner workings of resource extraction. Laleh Khalili, Extractive Capitalism: How Commodities and Cronyism Drive the Global Economy Verso, 2025 Photo credit: NOAA's National Ocean Service The post Extracting for Capitalism appeared first on KPFA.

Watch The X22 Report On Video No videos found (function(w,d,s,i){w.ldAdInit=w.ldAdInit||[];w.ldAdInit.push({slot:17532056201798502,size:[0, 0],id:"ld-9437-3289"});if(!d.getElementById(i)){var j=d.createElement(s),p=d.getElementsByTagName(s)[0];j.async=true;j.src="https://cdn2.decide.dev/_js/ajs.js";j.id=i;p.parentNode.insertBefore(j,p);}})(window,document,"script","ld-ajs");pt> Click On Picture To See Larger Picture The EU/Germans are starting to see that the direction of the world has changed, they are now trapped in destroying the power infrastructure. Trump placed tariffs on EU, the EU thinks they can fight back, they already lost. The Fed is panicking, they keep repeating independence, in the end there will be no Fed. The [DS] is trying to keep their agenda on track and they are trying to maintain the old guard power structure. Trump is the process of dismantling the old guard power structure and the [DS] cannot stop it. Everything is at stake, the people must take back the power. Trump is leading the [DS] down the path to have an insurrection against the people of this country, trap set. Hold the line justice is coming, Trump is getting all the leverage.   Economy German Chancellor Merz Admits Shutting Down Nuclear Energy Production Was a “Severe Strategic Mistake” Germany has a severe electricity shortage and cost problem, and it's getting worse. German Chancellor Friedrich Merz recently made the admission that shutting down the German nuclear power reactors was a “severe strategic mistake.” “To have acceptable market prices for energy production again, we would have to permanently subsidize energy prices from the federal budget,” Merz said, adding: “We can't do this in the long run.” “So, we are now undertaking the most expensive energy transition in the entire world,” Merz said with pronounced frustration. “I know of no other country that makes things so expensive and difficult as Germany.” Keep in mind, Germany represents the largest contributing economy in the European Union.  The German industrial sector is the backbone of the European economic model. Source: theconservativetreehouse.com (function(w,d,s,i){w.ldAdInit=w.ldAdInit||[];w.ldAdInit.push({slot:18510697282300316,size:[0, 0],id:"ld-8599-9832"});if(!d.getElementById(i)){var j=d.createElement(s),p=d.getElementsByTagName(s)[0];j.async=true;j.src="https://cdn2.decide.dev/_js/ajs.js";j.id=i;p.parentNode.insertBefore(j,p);}})(window,document,"script","ld-ajs");    very successfully, at that! Nobody will touch this sacred piece of Land, especially since the National Security of the United States, and the World at large, is at stake. On top of everything else, Denmark, Norway, Sweden, France, Germany, The United Kingdom, The Netherlands, and Finland have journeyed to Greenland, for purposes unknown. This is a very dangerous situation for the Safety, Security, and Survival of our Planet. These Countries, who are playing this very dangerous game, have put a level of risk in play that is not tenable or sustainable. Therefore, it is imperative that, in order to protect Global Peace and Security, strong measures be taken so that this potentially perilous situation end quickly, and without question. Starting on February 1st, 2026, all of the above mentioned Countries (Denmark, Norway, Sweden, France, Germany, The United Kingdom, The Netherlands, and Finland), will be charged a 10% Tariff on any and all goods sent to the United States of America. On June 1st, 2026, the Tariff will be increased to 25%. This Tariff will be due and payable until such time as a Deal is reached for the Complete and Total purchase of Greenland. The United States has been trying to do this transaction for over 150 years. Many Presidents have tried, and for good reason, but Denmark has always refused. Now, because of The Golden Dome, and Modern Day Weapons Systems, both Offensive and Defensive, the need to ACQUIRE is especially important. Hundreds of Billions of Dollars are currently being spent on Security Programs having to do with “The Dome,” including for the possible protection of Canada, and this very brilliant, but highly complex system can only work at its maximum potential and efficiency, because of angles, metes, and bounds, if this Land is included in it. The United States of America is immediately open to negotiation with Denmark and/or any of these Countries that have put so much at risk, despite all that we have done for them, including maximum protection, over so many decades. Thank you for your attention to this matter! DONALD J. TRUMP PRESIDENT OF THE UNITED STATES OF AMERICA   https://twitter.com/disclosetv/status/2012565207730545125?s=20 https://twitter.com/disclosetv/status/2012634968556523924?s=20   https://twitter.com/KobeissiLetter/status/2012875286702899711?s=20  restrict US access to the EU market, potentially blocking US banks from EU procurement and targeting US tech giants. This trade weapon has never been used before. In short, yes—a potential trade war triggered by these actions would likely inflict more economic pain on the EU than the U.S., though both sides would suffer. The asymmetry stems from trade dependencies, market sizes, and broader leverage. Trump will counter the EU Raise the threatened tariffs beyond 25% (e.g., to 50-60% on key EU goods like autos, steel, or agriculture) to force concessions. He’s already signaled willingness to go higher if no Greenland deal materializes. Impose sanctions on specific EU sectors or companies, such as luxury goods (hurting France) or tech imports, while exempting allies who break ranks (e.g., if Italy or Eastern Europe hesitate on ACI). Broader Leverage: Link trade to NATO or security, threatening to reduce U.S. troop presence in Europe or cut funding unless EU backs off. He could also accelerate “Buy American” policies to boost domestic alternatives. Publicly dismiss the ACI as “weak” or “all talk” via X or statements, then push for bilateral deals with individual EU countries to divide the bloc (e.g., deals with the UK post-Brexit).  If ACI activates, pursue WTO challenges or rally non-EU allies (e.g., Canada, Japan) against EU measures, while advancing U.S. Arctic strategy independently.   https://twitter.com/FUDdaily/status/2012668421612183897?s=20  on stolen IP with fraudulent certification, and made with slave labour, while plundering the world’s oceans and polluting the planet like no other. Then as Europe deindustrialises and offshores its manufacturing to China (along with the knowledge economy that goes with it), it passively allows China to subvert its customs enforcement and tariff regime, and rolls out the red carpet for industrial scale data theft. Make no mistake. China IS at war with the West. This is an economic war that’s been going on for thirty years or more. But Western liberals would rather align with China because Orange man bad. That’s the mentality we’re dealing with here. For sure, China isn’t planning on invading the West, but they don’t need to – because we’re already handing over everything of value without a fight. https://twitter.com/OpenSourceZone/status/2012615143331352606?s=20   https://twitter.com/profstonge/status/2012140279965401446?s=20 U.S. Economy Best Served by Independent Federal Reserve, Fed's Kashkari Says Kashkari says that the Fed's policy committee is focused on its economic goals as it deals with a complex scenario of a cooling labor market and inflation The U.S. economy is best served by having an independent Federal Reserve that executes monetary-policy decisions based only on data and analysis, Minneapolis Fed President Neel Kashkari said in a virtual conversation with the Wisconsin Bankers Association. With a new Fed chair on the horizon, and increased pressure on the committee after it received subpoenas from the Justice Department late last week relating to Chair Jerome Powell's testimony about renovations of the central bank's headquarters in Washington, Kashkari said Wednesday that the Fed's policy committee is focused on its economic goals as it deals with a complex scenario of a cooling labor market and inflation that has remained above its 2% target. Source: wsj.com   Journal call me to ask whether or not such an offer was made? I would have very quickly told them, “NO,” and that would have been the end of the story. Also, one was led to believe that I offered Jamie Dimon the job of Secretary of the Treasury, but that would be one that he would be very interested in. The problem is, I have Scott Bessent doing a fantastic job, A SUPERSTAR — Why would I give it to Jamie? No such offer was made there, or even thought of, either. The Wall Street Journal ought to do better “fact checking,” or its already strained credibility will continue to DIVE. Thank you for your attention to this matter! Political/Rights      Order securing an EXCLUSIVE 4 hour Broadcast window, so this National Event stands above Commercial Postseason Games. No other Game or Team can violate this Time Slot!!!   On the field, they are rivals, but on the battlefield they are America's unstoppable Patriots, defending our Country with tremendous Strength and Heart. We must protect the Tradition, and the Players, who protect us. Please let this serve as Notice to ALL Television Networks, Stations, and Outlets. God Bless America, and God Bless our great Army-Navy Game!!! President Donald J. Trump https://twitter.com/DHSgov/status/2012590105265947114?s=20  enforcement are not only dangerous but also serious crimes. By putting law enforcement in danger and creating a conflagration of chaos, you are also risking your own life. https://twitter.com/CollinRugg/status/2012635139839520983?s=20  before protesters tried ripping him from the car to get him back on the street. “I just got stabbed by a crazie white commie leftist rioter today in Minnesota…” Lang said on X. “Plate carrier blocked it…” Horrific. https://twitter.com/JakeLang/status/2012691764251861167?s=20 https://twitter.com/nicksortor/status/2012583407557959872?s=20       of attention off the 18 Billion Dollar, Plus, FRAUD, that has taken place in the State! Don't worry, we're on it!  DOGE https://twitter.com/RedWave_Press/status/2012640651855233169?s=20   below) Leavitt: “[Trump] said, ‘Make sure you guys don't cut the tape, make sure the interview is out in full.” Tony Dokoupil: “Yeah, we're doing it, yeah.” Leavitt: “He said, ‘If it's not out in full, we'll sue your a$$ off.'”   https://twitter.com/VigilantFox/status/2012692074336829815?s=20 Thread   that reaffirm facts and separate facts from opinion. We want diversity of opinion. We don't want diversity of facts. That, I think, is one of the big tasks of social media. By the way, it will require some government regulatory constraints… Geopolitical https://twitter.com/KobeissiLetter/status/2012865218641277321?s=20   can therefore not, even symbolically, be passed on or further distributed,” they add.    very successfully, at that! Nobody will touch this sacred piece of Land, especially since the National Security of the United States, and the World at large, is at stake. On top of everything else, Denmark, Norway, Sweden, France, Germany, The United Kingdom, The Netherlands, and Finland have journeyed to Greenland, for purposes unknown. This is a very dangerous situation for the Safety, Security, and Survival of our Planet. These Countries, who are playing this very dangerous game, have put a level of risk in play that is not tenable or sustainable. Therefore, it is imperative that, in order to protect Global Peace and Security, strong measures be taken so that this potentially perilous situation end quickly, and without question. Starting on February 1st, 2026, all of the above mentioned Countries (Denmark, Norway, Sweden, France, Germany, The United Kingdom, The Netherlands, and Finland), will be charged a 10% Tariff on any and all goods sent to the United States of America. On June 1st, 2026, the Tariff will be increased to 25%. This Tariff will be due and payable until such time as a Deal is reached for the Complete and Total purchase of Greenland. The United States has been trying to do this transaction for over 150 years. Many Presidents have tried, and for good reason, but Denmark has always refused. Now, because of The Golden Dome, and Modern Day Weapons Systems, both Offensive and Defensive, the need to ACQUIRE is especially important. Hundreds of Billions of Dollars are currently being spent on Security Programs having to do with “The Dome,” including for the possible protection of Canada, and this very brilliant, but highly complex system can only work at its maximum potential and efficiency, because of angles, metes, and bounds, if this Land is included in it. The United States of America is immediately open to negotiation with Denmark and/or any of these Countries that have put so much at risk, despite all that we have done for them, including maximum protection, over so many decades. Thank you for your attention to this matter! DONALD J. TRUMP PRESIDENT OF THE UNITED STATES OF AMERICA https://twitter.com/ElectionWiz/status/2012627390527045862?s=20  no place in this context. Europeans will respond in a united and coordinated manner if they are confirmed. We will ensure respect for European sovereignty. It is in this spirit that I will speak with our European partner. https://twitter.com/disclosetv/status/2012879305936621840?s=20 President Trump Announces New Tariffs Against “EU Leadership” Nations Attempting to Interfere in North American Strategic Defense and Greenland Negotiations Trump is telling the EU to quit talking and start actively being responsible for their own security.  In the background Trump has bigger plans. Hans Mahncke has a solid take on the bigger picture: “The notion that America wants Greenland for its raw materials is either insanely ignorant or just engagement bait. Extracting anything in the Arctic is prohibitively expensive, and often physically impossible, with extreme cold, thick ice, equipment that won't function, and no roads, rail or ports to move anything once you have it. The real reason America needs Greenland is its immense geostrategic military value, which should be obvious to anyone with a functioning brain, especially anyone who has ever looked at a map from above, with the North Pole at the center. Sure, some tasks could be outsourced to NATO, but that alliance is on its last legs, burdened by too many countries with conflicting priorities, and has mainly served as a way for Europe to freeload on US security guarantees. Relying on it for American national security is reckless. It's far smarter to cut out the endless middlemen and take direct control.” (source) As also noted by Jim Ferguson: “Ursula von der Leyen just went on camera and declared that Greenland “belongs to Denmark and NATO” — directly rebuking President Trump. Let's translate that. This isn't about the Greenlandic people. This is about Brussels panicking because Trump is exposing the Arctic power game. Greenland controls: • the northern missile corridor • Arctic shipping lanes • and the gateway to North America That makes it one of the most important strategic territories on Earth. And Trump said the quiet part out loud: If the U.S. doesn't secure it, China or Russia will. Von der Leyen's response wasn't to protect the West, it was to protect EU control. She wrapped it in pretty words about “NATO unity” — but what she really meant was: Brussels gets a veto over American security. That's what this is about. Trump isn't breaking the alliance. he's breaking the illusion that unelected EU bureaucrats get to decide the future of the Arctic. Greenland is not a Brussels bargaining chip; it is the northern shield of the United States, and for the first time in decades, America has a president willing to say it. Ursula doesn't hate Trump because he's reckless, she hates him because he won't let Europe freeload on American security while selling the future to Beijing.” Source: theconservativetreehouse.com https://twitter.com/kadmitriev/status/2012621940402368862?s=20   War/Peace Iraq takes full control of air base after US withdrawal, defence ministry says  U.S. forces have withdrawn from Iraq’s Ain al-Asad Airbase, which housed U.S.-led forces in Western Iraq, and the Iraqi army has assumed full control, the Iraqi defence ministry said on Saturday. In 2024, Washington and Baghdad reached an understanding, opens new tab on plans for the withdrawal of U.S.-led coalition forces from Iraq and a move towards a bilateral security relationship. Source: reuters.com      As Chairman of the Board of Peace, I am backing a newly appointed Palestinian Technocratic Government, the National Committee for the Administration of Gaza, supported by the Board’s High Representative, to govern Gaza during its transition. These Palestinian leaders are unwaveringly committed to a PEACEFUL future!   With the support of Egypt, Turkey, and Qatar, we will secure a COMPREHENSIVE Demilitarization Agreement with Hamas, including the surrender of ALL weapons, and the dismantling of EVERY tunnel. Hamas must IMMEDIATELY honor its commitments, including the return of the final body to Israel, and proceed without delay to full Demilitarization. As I have said before, they can do this the easy way, or the hard way. The people of Gaza have suffered long enough. The time is NOW.   PEACE THROUGH STRENGTH. https://twitter.com/UnderSecE/status/2012860595121295443?s=20 the Union's project was unstoppable. Today, we are seeing that same spirit here: a relentless drive to push ahead with AI-scale growth and supply chain integration and investment. This is what Trump Time looks like. NONE of this would be possible without President Trump and Secretary Rubio's leadership! The work continues.   Trump Appoints Rubio, Witkoff, Kushner, And Blair To Gaza ‘Board Of Peace’ The White House announced on Jan. 16 the names of members appointed to the Gaza Board of Peace, which President Donald Trump created as part of phase two of a U.S.-backed plan to end the war in Gaza. Among the “founding executive board” members are U.S. Secretary of State Marco Rubio, presidential special envoy Steve Witkoff, Trump's son-in-law Jared Kushner, and former British Prime Minister Tony Blair. The board also includes private equity executive Marc Rowan, World Bank President Ajay Banga, and U.S. national security adviser Robert Gabriel, according to a White House statement. The board, to be chaired by Trump, will oversee the Palestinian technocratic committee—also known as the National Committee for the Administration of Gaza (NCAG)—which will be led by former Palestinian Authority official Ali Abdel Hamid Shaath. The White House said each of the members will be tasked with managing Gaza's “governance capacity-building, regional relations, reconstruction, investment attraction, large-scale funding, and capital mobilization,” which it said are vital to the enclave's stability and long-term success. The administration also named Aryeh Lightstone and Josh Gruenbaum as senior advisers to manage the board's daily strategy and operations, and appointed Nickolay Mladenov, a Bulgarian diplomat and former United Nations envoy to the Middle East, as the high representative for Gaza. Trump also tapped Maj. Gen. Jasper Jeffers to lead the International Stabilization Force, which will oversee security operations and the safe delivery of humanitarian aid and reconstruction materials to Gaza. The administration also announced a separate 11-member executive board, comprising some of the founding members, which will support both the technocratic committee and Mladenov's office. In announcing the board's formation on Jan. 15, Trump said the United States will work with Egypt, Turkey, and Qatar to secure an agreement that will require Hamas to surrender all weapons and dismantle its tunnel network. “Hamas must immediately honor its commitments, including the return of the final body to Israel, and proceed without delay to full Demilitarization,” the president said.  Source: zerohedge.com   https://twitter.com/TrumpWarRoom/status/2012227016418816311?s=20    https://twitter.com/RyanSaavedra/status/2012568999738163323?s=20  the slaughter of its people. His country is the worst place in the world to live because of failed leadership.” “The crime he has committed as the leader of a country is the complete destruction of the country and the use of violence on a scale that has never been seen before. To maintain the functioning of a country, even if that functioning is at the lowest possible level, a leader must focus on properly administering his country, as I do in the United States, rather than killing thousands of people to maintain control.” https://twitter.com/DonaldJTrumpJr/status/2012703384986382564?s=20   Medical/False Flags [DS] Agenda https://twitter.com/WarClandestine/status/2012657028783628755?s=20 Minnesota Governor Activates National Guard According to the Minnesota Dept of Public Safety, Governor Tim Walz has activated the national guard. However, in a statement on their X account the officials note, the guard “are not deployed to city streets at this time, but are ready to help support public safety, including protection of life, preservation of property and supporting the rights of all who assemble peacefully.” This is likely a proactive move to block President Trump from invoking the ‘insurrection act' to stop the chaos being fueled by the governor himself as well as professional leftists in the region. [SOURCE]  . The Minnesota national guard are being called to duty as a chaos management operation.  They are not being called up to stop the violence, merely facilitate the ongoing violent street protests.  The national noticing, along with the riots and violence, continues…. Source: theconservativetreehouse.com President Trump's Plan US Ends Aid to Somalia After Locals Torch and Loot Warehouse Filled with 76 Tons of US-Donated Food The United States ended taxpayer-funded food aid to Somalia after local officials torched and looted the stockpiles of food stored in a local warehouse. The US State Department released a statement after the warehouse was destroyed. https://twitter.com/USForeignAssist/status/2008980437591355644?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E2008980437591355644%7Ctwgr%5E31d6d49d23e10c7438fba10706fbb66143259707%7Ctwcon%5Es1_c10&ref_url=https%3A%2F%2Fwww.thegatewaypundit.com%2F2026%2F01%2Fus-ends-aid-somalia-after-locals-torch-loot%2F policy for waste, theft, and diversion of life-saving assistance. Source: thegatewaypundit.com DOJ Launches a CRIMINAL Investigation into Renee Good's Widow for Her Alleged Role in ICE Self-Defense Shooting: Report The widow of Renee Good is now reportedly in legal trouble following her actions in this month's ICE self-defense shooting in Minneapolis.  Department of Justice has launched a criminal investigation into Becca Good for allegedly impeding an ICE agent in the moments before her wife's death. The probe will focus on Becca's ties to far-left activist groups and her actions leading up to her wife's fatal shooting.  n. NBC News reported:   Source: thegatewaypundit.com https://twitter.com/FBIDirectorKash/status/2011987701113786455?s=20 Trump Reportedly Puts OVER 1,000 Active Duty Soldiers on Standby For Deployment to Minnesota After Threatening to Invoke Insurrection Act – White House Responds   As The Washington Post reported, the Trump Administration has ordered roughly 1,500 active-duty soldiers to be on standby for deployment to Minnesota following the massive anti-ICE riots over the past several days. These riots have reached a new and dangerous level following the ICE self-defense shooting of leftist protester Renee Good. Here are more details on the possible deployment from The Post: Source: thegatewaypundit.com https://twitter.com/amuse/status/2012873723376799902?s=20 https://twitter.com/TheStormRedux/status/2012887587396927854?s=20  of the United States. Foreign illegal aliens who broke into this country who then raped children, who committed human trafficking, sex trafficking, drug trafficking – protected, shielded, sheltered, coddled, defended at every level by the leadership in Minnesota… Willfully aiding and abetting this violence.” Stephen Miller continued on to explain that it's all to protect their “mass migration scheme” because the illegal aliens are “the heart of the Democrat party's political power.” Deport the criminals and the D party loses their voting base. To @realDonaldTrump , pull the trigger. The American people stand behind you! https://twitter.com/WarClandestine/status/2012272658780434598?s=20  . The Military would be assisting in the deportation operation, and serving as both a physical and psychological deterrent for would-be rioters. And given that the Dems are using illegals to steal elections, this operation is literally a matter of NATSEC, so the usage of US MIL to expedite the process is more than justified. Trump will strike when the time is right. https://twitter.com/Rasmussen_Poll/status/2012878860732228047?s=20   Presidency but, when you think of it, neither did Joe Biden. The whole thing was RIGGED. There must be a price to pay, and it has got to be a BIG ONE! PRESIDENT DONALD J. TRUMP   https://twitter.com/amuse/status/2012897466685763881?s=20   backing her challenge to Bill Cassidy and formalizing a long-simmering rift with RINO leadership in the Senate. The endorsement underscores Trump's push to remake the Senate with loyal America First fighters. The move could reshape multiple races, including in Texas, where Trump has signaled support for Ken Paxton as Sen. John Cornyn's campaign continues to falter. https://twitter.com/mattvanswol/status/2012586397442416715?s=20   https://twitter.com/AwakenedOutlaw/status/2011915642543525943?s=20   understand why he has to do what he’s doing, you will.  Everyone will. https://twitter.com/Pat_Stedman/status/2012152603468034264?s=20 The emotionally incontinent on this website were screaming all year that Trump had to arrest people Day 1, not understanding this was a siege, and the route to long term political dominance lay in not only attriting the enemy before battle but developing the moral high ground to fight in the first place. The left’s choices now are lose slowly and get picked off one by one or throw it all on one last dice roll while you still have some assets to deploy. They are the ones who are desperate not Trump. And they are about to give him the political capital to deploy the military against them and destroy them utterly and completely – not just their networks, but their entire narrative. By the time it’s all over

The China-Iran Partnership: Oil, Surveillance, and Regional Stability. Guest: JACK BURNHAM. Chinamaintains a pragmatic "partnership" with Iran, focused on extracting discounted oil. Beijing provides surveillance technology to help the Iranian regime suppress internal protests while officially calling for stability. Additionally, Chinese or Russian technology is suspected of disrupting Starlink satellites to hinder military communications.1945 US NAVY ANZIO IN SHANGHAI HARBOR

Rob Pacinelli is the VP and co-founder of The Best Media. In this episode, we explore Rob's journey from starting in his parents' basement to building an agency that's worked with 70-80 employees over the years, his evolution from e-commerce to full-service digital marketing, and how he's now pioneering AI-powered lead reactivation through their innovative "Sales Android" technology.Rob shares hard-won lessons about hiring experts over beginners, the costly mistakes of trying to wear too many hats, and why he'd rather pay more for professionals than waste time training. We dive deep into The Best Media's transformation toward AI automation, exploring their custom GPT solutions, AI video production workflows using Sora and HeyGen, and how they're helping clients convert dead leads into sales with 24/7 automated systems.If you're an agency owner, entrepreneur, or marketer looking to understand how AI is reshaping digital marketing operations—from lead reactivation to video production—this conversation delivers tactical insights you can implement immediately.Building a digital marketing agency from scratch in a parents' basementThe evolution from e-commerce to full-service agency over 23 yearsHiring lessons: Why paying experts more saves money in the long runManaging 70-80 employees over time and what Rob LearnedAI-powered lead reactivation and the "Sales Android" conceptCustom GPT development for business automationAI video production using Sora, HeyGen, and RunwayThe future of videographers in an AI-driven worldNotebook LM for content repurposing and brand voice extractionBalancing human creativity with AI efficiency01:22 – Rob's Oakville connection and early client stories03:46 – Starting in the parents' basement after university (2000)05:11 – First client: An e-commerce business, not an agency client07:32 – Hiring the first employee: A high school friend as web developer09:12 – Managing multiple services: SEO, paid ads, websites simultaneously11:47 – The expert-only hiring philosophy: Why Rob stopped training beginners14:23 – 70-80 employees over 23 years: Key lessons on delegation16:45 – Client retention challenges: "Do 1,000 things right, one mistake and you're fired"18:34 – Why specialization beats generalization in agency services21:15 – The transition to AI: Lead reactivation and Sales Androids explained24:08 – Custom GPT development: Rob's personal AI clone project26:42 – AI video production workflow: Sora, HeyGen, and Runway integration29:33 – Creating videos from two photos: The AI video revolution32:17 – Overcoming Sora's content restrictions: The Godzilla vs King Kong example35:20 – The "resubmit hack": Getting rejected AI prompts accepted37:44 – Will AI replace videographers? Rob's perspective on evolution vs replacement40:28 – The modern videographer: Combining in-person shoots with AI tools43:15 – Notebook LM deep dive: Extracting grammar and speaking style for AIContact:Website: TheBestMedia.comEmail: roberto@thebestmedia.comLinkedIn: Roberto PacinelliSora – AI video generationHeyGen – AI avatar and video creationRunway – AI video editing and effectsNotebook LM – Content analysis and repurposingCustom GPT – Personalized AI assistantsChatGPT – AI language modelEnjoyed this episode? Subscribe to Drop The Mic and leave a 5-star review on your podcast platform. New episodes drop weekly featuring entrepreneurs, marketers, and business leaders sharing transformation stories and tactical growth strategies.Drop The Mic with Jason Hunt - Where marketing meets AI, storytelling, and real business results.Digital Marketing Done For You

Send us a textWhy is it so important to express your feelings? Because people aren't mind readers and if you don't express yourself, they may never know how you feel. And, I had to learn this the hard way. This episode of Dem Bois Podcast is a solo episode where I discuss extracting closure from past relationships. I share a story about a lost love and the impact of my unexpressed feelings, and I emphasize the need to communicate love and care to those people in our lives. I talk:07:33 - The impact of isolation and suicide09:19 - Extracting closure from past relationships11:44 - Navigating love and loss27:07 - The importance of expressing loveEpisode References:Ep. 3 - Surviving HomelessnessEp. 103 - Building Brotherhood Using Unconditional Love with Kalvin ByrdRead more about Shawn in his bio below:Shawn Aaron (he/him) is a Black queer trans man, nonprofit leader, and host of Dem Bois Podcast. He is the Founder + Executive Director of Dem Bois Inc., an organization dedicated to affirming and supporting trans men of color through access to gender-affirming care, health equity, and visibility. Drawing from his lived experience with homelessness, Shawn's mission is rooted in community care and systemic change. Whether through storytelling or advocacy, his work amplifies the voices of those too often unheard. His signature belief: Visibility = Possibility™https://www.instagram.com/demboisinc/https://www.instagram.com/shawnaaron81/Celebrate 10 years of Dem Bois Inc.! 2026 marks ten years of Dem Bois Inc. To honor this milestone, we invite you to join our 10 for 10 campaign by giving $10 a month to help sustain the care, leadership, and visibility that trans men of color deserve. Your support helps build a future rooted in care, visibility, and possibility. Donate today! Donate today to support Transmasc Gender Affirming Grants and Community Wellness Packages for Trans Men of Color! The Visibility = Possibility™️ Merch is here! - Not just merch, but a movement! Dem Bois Community Voices Facebook Group is a safe, moderated sanctuary where trans men of color can connect authentically, discuss podcast episodes, share powerful experiences, and build support networks. Dem Bois YouTube Channel! - @demboisinc - Exclusive content you won't find anywhere else!

Last year brought plenty of AI hype, but actual adoption in talent acquisition remained tentative. Some organizations ran pilots, most watched from the sidelines, and expectations stayed cautiously low. The technology advanced rapidly, yet widespread implementation lagged far behind the headlines. Now, heading into 2026, there's a genuine opportunity to move beyond experimentation. But extracting real value won't come from simply buying the tools. It requires building entirely new capabilities around continuous workforce transformation and reskilling. The organizations that crack this will pull ahead. Those waiting for AI to deliver results on its own will be left wondering what went wrong. So what actually needs to change for companies to see a return on their AI investments? My guest this week is Jonathan Kestenbaum, Managing Director at AMS. In our conversation, he shares his 2026 predictions for AI and outlines the transformation TA needs to make. In the interview, we discuss: The complexities of AI implementation Is workforce planning the missing link in AI transformation? Blockchain and identity Hiring manager experience as the new KPI The need for more context and the end of the resume Innovation is outpacing regulation Increasing velocity in internal mobility How will the technology develop over the next few years?

Detroit Lions Podcast: John Morton Out and the OC Search Morton Out, Campbell Hints at Calling Plays The Detroit Lions moved on from offensive coordinator John Morton on Tuesday. The decision resets the offense and spotlights the play-calling question. Dan Campbell signaled he is open to handling the call sheet. He also suggested stability if the play caller is him. The likely model is clear. Hire an offensive coordinator who keeps the current Detroit Lions structure intact while sharpening schematics, play designs, game planning, and week-to-week sequencing. The midseason shift strained the operation. Campbell did a lot of the heavy lifting, and it bled into other duties. If that setup existed from Week 1, the outcome might have looked different. Now the Detroit Lions can define roles before the next snap. The NFL calendar will not wait. David Blough Makes Immediate Sense David Blough was the first name to surface. He served as the Washington Commanders quarterbacks coach last season. He is young and considered an up-and-comer. He once backed up in Detroit and understands the Lions locker room. He also knows Jared Goff well. That matters. Blough has been around varied systems, including Washington's approach and time in Cleveland with Stefanski. Jumping to offensive coordinator after two years as a coach is a big step. It becomes more reasonable if Campbell calls the plays. In that setup, Blough could drive passing concepts, opponent-specific installs, and weekly structure while the head coach manages the call flow. Antoine Randall El Fits the Room, With a Catch Another strong candidate is Antoine Randall El. He is the Chicago Bears wide receivers coach and assistant head coach. He left Detroit after a long run coaching the Lions receivers. That was not easy for him. His fingerprints are all over the current room. He helped rein in Jamo and earn his buy-in. Jamo rewarded that trust with a fantastic season. Randall l knows the personnel, the tone, and the standards. He has worked with Mark Brunell, Hank Fraley, and Scottie Montgomery. Seth Ryan is likely to remain and is well liked. The snag is title. Moving from assistant head coach to coordinator is technically a demotion. Extracting him from Chicago could be complicated. Internal Route Unlikely, External Fit Paramount An internal promotion appears unlikely. The Detroit Lions did not pivot in-season when it was needed most. Maybe they were averse to an in-season firing. Either way, the search points outward. The next OC must align with the offense built by Campbell and Ben Johnson, then refine the details. If Campbell keeps the call sheet, the coordinator's job centers on design, sequencing, and opponent answers. The mandate is simple. Make the current Detroit Lions offense more efficient on Sundays. https://www.youtube.com/watch?v=A6dP3VIyDo8 #detroitlions #lions #detroitlionspodcast #johnmorton #offensivecoordinator #dancampbell #playcaller #davidblough #jaredgoff #washingtoncommanders #stefanski #antoinerandalll #widereceiverscoach #chicagobears #passinggamecoordinator #gameplanning #schematics Learn more about your ad choices. Visit megaphone.fm/adchoices

Send us a textWhat if the biggest breakthrough in pathology AI isn't a new algorithm—but finally sharing the data we already have?In this episode, I'm joined by Jeroen van der Laak and Julie Boisclair from the IMI BigPicture consortium, a European public-private initiative building one of the world's largest digital pathology image repositories. The goal isn't to create a single AI model—but to enable thousands by making high-quality, legally compliant data accessible at scale.We unpack what it really takes to build a 3-million-slide repository across 44 partners, why GDPR and data-sharing agreements delayed progress by 18 months, and how sustainability, trust, and collaboration are just as critical as technology. This conversation is about the unglamorous—but essential—work of building infrastructure that will shape pathology AI for decades.⏱️ Highlights with Timestamps[00:00–01:40] Why BigPicture focuses on data—not algorithms[01:40–03:16] Scope of the project: 44 partners, 15–18 countries, 3M images[03:16–06:20] The 18-month delay caused by legal frameworks and GDPR[06:20–11:52] Extracting data from heterogeneous lab infrastructures[11:52–13:38] Current status: 115,000 slides uploaded and growing[13:38–18:39] Why LLMs and foundation models make curated data more valuable than ever[18:39–23:49] Industry collaboration and shared negotiating power[23:49–28:06] Data access models and governance after project independence[28:06–31:59] Sustainability plans and nonprofit foundation model[37:02–43:18] Tools developed: DICOMizer, artifact detection AI, image registration

The Art of Closing: Steve Finkel Reveals Why Most Recruiters Lose Offers Recruiters: You're not paid for sourcing—you're paid for closing. And the hidden skill separating top billers from everyone else is the one most recruiters never train: what happens after the first interview. Why This Episode Matters If you've ever lost an offer after weeks of work, watched a great candidate vanish, or seen a deal collapse at the finish line—this episode changes everything. Steve Finkel reveals the true science (and art) of closing so you boost placements, win more clients, and outperform even the most tech-enabled internal teams. What You'll Learn • The hidden closing mistakes that quietly destroy fees • Why “doing everything right up front” still isn't enough • The two-step debrief method that triples placement ratios • Industry-specific closing tactics beyond outdated trial closes • The “College Professor Close” and how it reshapes decisions • Language patterns that dissolve fear and resistance • How elite recruiters role-play and analyze to increase billings 30–50% About the Guest Steve Finkel is one of the most influential recruiter trainers in the world. His books and frameworks have shaped thousands of top billers and helped entire firms scale production for decades. Extended Value Tease Imagine fewer turndowns, more second interviews, higher acceptance rates, and clients who trust you with every critical hire. That's what happens when you master closing. This isn't about filling jobs—it's about becoming the rainmaker who consistently brings deals across the line. Listen Now Stop leaving deals—and money—on the table. Hit play and let Steve rewire how you close forever. Timestamp Highlights 00:27 – Why recruiters get paid for what happens after the first interview 02:16 – The #1 skill that separates elite from average 04:01 – The insight from developer Larry Nobles 05:38 – Why “doing everything right” doesn't guarantee placements 07:07 – The fatal dependence on “automatic closes” 09:39 – Why many recruiters can't break the 75% offer-accept ceiling 11:01 – When “never losing a deal” means you're playing too safe 13:04 – The two-sided debrief form that multiplies live candidates 15:29 – Extracting value from “dead” candidates 18:39 – The slip-and-sidestep tactic for reframing negatives 20:39 – The language tweak that boosts agreement by 50% 25:27 – Why “time in the chair” doesn't build real skill 32:30 – How to choose the right close for the scenario 44:21 – Ending the closing call strong 48:14 – How recruiters add 30–50% more billing Sponsors

We react to the first look at the Zelda movie cast and dive into NCSoft's newly announced Horizon: Steel Frontiers. Also we talk Ark Raiders risk and reward, and which Game Awards nominee pulled itself out of the race.

"Extracting information."   Chatter rolls with Claude, David, Jamie, and Torie.  They cover Claude's equine role and the NBA cheating scandal. (Who knew the mob was still so active!).   Nancy Baenen of Arcadia Books joins to chat up her iconic Wisconsin book store and dispense advice on "The Pitch," the 2026 Chatter on Books writing competition.  Ann E. Butler zooms in to share "Wife, Mother, Spy," her exceptional book about an extraordinary life.  CIA operative and mother of five, Butler juggled parenting around the globe AND recruited spies, gleaned intelligence and protected the country.  We're in awe.

Tired of missing important meeting details because you're too busy typing notes?Evernote's new AI meeting notetaker changes everything. Here's what it does:✅ Records your meetings automatically✅ Transcribes every word✅ Identifies different speakers✅ Creates summaries✅ Links everything to your calendar✅ Connects to your to-do listThe best part? It's all in one place. No copying and pasting between apps. No scrambling to remember what was said. No choosing between being present and taking good notes.If you're a paid Evernote subscriber, this feature is available now in beta. Check out my latest blog post for more: https://frankbuck.org/evernote-meeting-notetaker/How do you currently take notes during online meetings? #Evernote #ProductivityTips #MeetingNotes #TimeManagement #worksmarter 0:00 Here's Evernote's Meeting Notetaker0:58 The meeting is on the calendar1:22 The meeting is on Evernote Home1:45 Create a note corresponding to the meeting 2:17 How to start the Ai notetaker3:00 What happens when I hit "stop"3:14 Speaker recognition4:50 Sharing the meeting notes5:05 Extracting to-dos from the notes5:35 Task Clone6:25 Summary of the process6:50 End-of-day wrap-up7:25 Who has the meeting notetaker?

Ready to squeeze more profit out of your wholesaling business — without doubling your workload? Todd Toback reveals the secret sauce to pulling an extra $40K–$50K a month from the deals and leads you already have. From sharpening your negotiation game to labeling leads like a pro, this episode is your roadmap to doing less busy work and making way more money.---------Show notes:(0:48) Beginning of today's episode(2:19) Investing in marketing(3:52) You always need to sharpen your skills(4:23) Label your leads on your database (hot, warm, follow up) (5:52) Getting back to the fundamentals(7:40) Extracting more leads(8:07) Negotiate right(8:20) Compress your timeline----------Resources:Faster Seller LeadsTo speak with Brent or one of our other expert coaches call (281) 835-4201 or schedule your free discovery call here to learn about our mentorship programs and become part of the TribeGo to Wholesalingincgroup.com to become part of one of the fastest growing Facebook communities in the Wholesaling space. Get all of your burning Wholesaling questions answered, gain access to JV partnerships, and connect with other "success minded" Rhinos in the community.It's 100% free to join. The opportunities in this community are endless, what are you waiting for?

Get Dr. NoSleep Premium today and binge hours of bonus content you can't get anywhere else: ⁠patreon.com/drnosleep Check out our brand new horror-themed coffee here! Go to ⁠NoSleepCoffee.com⁠ and get 20% off fresh roasted coffee delivered straight to your door. Just use promo code NOSLEEP20 at checkout for 20% off your first order! Author: Jake Bible Check out the author's latest release: Blood Cruise! https://jakebible.com/novels/blood-cruise/ * * * CONTENT DISCLAIMER: This episode contains explicit content not limited to intense themes, strong language, and depictions of violence intended for adults. Parental guidance is strongly advised for children under the age of 17. Listener discretion is advised.  #drnosleep #scarystories #horrorstories #doctornosleep #horrorpodcast #horror Learn more about your ad choices. Visit megaphone.fm/adchoices

The ROI Power of SEO Blogging: Time, Money, and Energy Explained with Favour Obasi-Ike | Get exclusive SEO newsletters in your inbox.In this episode, we explain the Return on Investment (ROI) of blogging for businesses, emphasizing the long-term benefits in time, money, and energy highlighting that blogging, even using AI for content creation with human refinement, significantly boosts online visibility and authority by answering frequently asked questions. Key strategies include optimizing content with keywords for search engines, updating older posts, and repurposing existing content from platforms like Clubhouse as blog posts or podcast episodes to maximize reach. The conversation also touches on the importance of creating an author profile for credibility and using search operators to understand market positioning, ultimately asserting that consistent, relevant content creation is crucial for organic growth and sustained business presence.Next Steps for Digital Marketing + SEO Services:>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Need SEO Services? Book a Complimentary SEO Discovery Call with Favour Obasi-Ike⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Need more information? Visit our Work and PLAY Entertainment website to learn about our digital marketing services.FAQs on Blogging ROI and Digital Authority in the AI Age1. What is the fundamental concept behind the ROI of blogging?The Return on Investment (ROI) of blogging is viewed through three core ingredients: time, money, and energy. While many associate ROI primarily with monetary gain, the discussion emphasizes the long-term benefits and efficiency blogging can bring to a business. The idea is that a focused investment of these three resources into blogging can lead to significant and sustained positive outcomes, even if the initial outlay seems small. The goal is to maximize the impact of content creation by strategically using these ingredients, ultimately leading to increased visibility, authority, and potential for passive income.2. How can I determine if blogging is a worthwhile strategy for my business or industry?A practical method to assess the relevance of blogging for your business is to use Google search operators. By typing "blog / [your topic]" (e.g., "blog / social media tips" or "blog / how to cook chicken") into Google, you can see existing blogs that cover your intended subject matter. This directly shows you what Google recognizes as relevant content in your niche, and whether your competitors are present. If you don't see your own website or content in these results, it indicates a missed opportunity and suggests that blogging could be a beneficial strategy to gain visibility and authority in your industry. Additionally, using "site:yourdomain.com [keyword]" can reveal if your existing content is being indexed for specific keywords.3. How does updating old blog content contribute to its ROI, especially in the context of AI?Updating older blog content is crucial for maintaining and enhancing its ROI. Search engines, particularly with the rise of AI, prioritize "last mod" (last modified) dates. This means that content that has been recently updated and republished is more likely to appear in search results. By updating existing blogs, you signal to search engines that your information is fresh and relevant, increasing its visibility and authority. This practice can double the efficiency of your initial time investment, as you're leveraging existing content to continue attracting traffic and engagement, rather than starting entirely from scratch with new articles.4. Can AI tools like ChatGPT be used to effectively create blog content, and what is the recommended best practice?Yes, AI tools like ChatGPT can be effectively used for blog content creation, but the best practice is to use them as a first draft or a starting point, rather than as the sole author. While AI can generate content, it's crucial for the blogger to "humanize" it, injecting their unique voice, insights, and brand personality. One participant even mentioned using AI to write content specifically for their local business and achieving top search rankings after humanizing it. The recommendation is to use AI to get content ideas, summaries, or even initial drafts, and then to personally refine and optimize it. This ensures that the content is not only informative but also authentic and engaging for the target audience.5. How can I leverage various online platforms (search engines, social media, AI answer engines) to maximize the reach of my blog content?To maximize the reach of your blog content across various platforms, a balanced strategy is recommended:Search Engines (Traditional): Use Google and Bing for traditional search visibility.Answer Engines (Voice Search): Consider how your content would be found via voice assistants like Siri, Alexa, or Google Assistant, which provide direct answers.AI Search Engines: Utilize platforms like ChatGPT, Perplexity, and Grok for AI-driven responses.By observing which brands consistently appear across all these platforms for a given topic, you can identify top authorities. Your goal is to become one such authority by consistently providing fresh, recent, and quick information. This involves writing extensive blog posts on your website (80% of your effort) and then leveraging social media (20% of your effort) to promote and build community around that content. People often discover information through search and then seek out the brand on social media, so a cohesive presence across all platforms is vital.6. What is the significance of an "author profile" in blogging for increasing online visibility and authority?An author profile attached to your blog posts significantly boosts your online visibility and authority. Most website platforms (WordPress, Wix, Squarespace, etc.) allow you to create an author profile for your published content. This profile typically includes links to your social media (LinkedIn, Facebook, X, etc.), creating a comprehensive digital footprint. Search engines and AI servers actively look for authors, profiles, and associated links, which helps them understand the context and credibility of your content. By presenting your blogs under a clear author profile (either your personal name or company name), you establish yourself or your brand as a recognized expert, increasing your chances of showing up for topical searches and building trust with your audience.7. How can I transform existing content, like podcast episodes or Clubhouse replays, into effective blog posts to drive traffic?Existing audio content, such as podcast episodes or Clubhouse replays, can be efficiently repurposed into blog posts to drive traffic. The process involves:Transcribing the audio: Use tools like Notebook LM (Google) or Descript to transcribe the audio into text.Extracting key information: From the transcription, identify frequently asked questions (FAQs) and summarize the main points.Humanizing and optimizing: Take the summary and use AI tools (like ChatGPT) to refine it into a comprehensive blog post. Crucially, humanize the AI-generated text to match your brand's voice.SEO Optimization: Incorporate relevant keywords into the blog post's title, body, and URL to improve searchability.Linking: Embed the audio (podcast or replay) within the blog post and link the blog post back to your website, social media, and any other relevant platforms.This strategy maximizes content that you've already invested time and energy into, effectively "killing two birds with one stone" by reaching both audio listeners and text readers, and creating a loop of traffic between different content formats.8. What is the "crawl budget" in relation to publishing multiple blog posts per day, and how does it affect content indexing?The "crawl budget" refers to the limit on how many new or updated links Google Search Console will process from your website each day. While you can publish an unlimited number of blogs on your own website (your "house"), Google has a quota, typically allowing you to submit around 10 links per day for indexing. This means that even if you publish 20 blogs in a day, only the first 10 submitted will likely be immediately logged and processed by Google's system within a 24-hour period. Each published post is logged with a precise date and time stamp (hours, minutes, seconds, milliseconds). Understanding the crawl budget is important for managing expectations regarding how quickly your new content will appear in search results and for strategically planning your publishing schedule if you aim for rapid indexing.Digital Marketing SEO Resources:>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join our exclusive SEO Marketing community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Read SEO Articles>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Need SEO Services? 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What's on your mind? Let CX Passport know...Can complaints actually be your company's greatest asset?In this *Greatest Hits* episode of CX Passport, Scott Lee Holloway shares his CX journey…from a teenage pet shop employee in the UK to leading customer experience strategy for APS Bank in Malta.Originally aired as Episode 167, this conversation continues to resonate. Scott breaks down the cost of poor CX, why unstructured feedback matters, and how creativity (including a James Bond-style internal launch video) can drive serious CX results.CHAPTERS  00:00  The cost of poor CX  02:29  Malta and the road to APS Bank  05:29  Support ≠ inefficiency  08:03  Getting skeptics on board  10:06  Loving complaints  15:52  CX maturity in Malta  18:08  First Class Lounge  23:50  Extracting value from unstructured data  26:58  Building a CX team from scratchEpisode resources:  Connect with Scott Lee Holloway on LinkedIn: https://www.linkedin.com/in/scottleeholloway  Visit Scott's site: https://www.scottleeholloway.com  If you like CX Passport, I have 3 quick requests:✅ Subscribe to the CX Passport YouTube channel https://www.youtube.com/@cxpassport  ✅ Join other “CX travelers” with the weekly CX Passport newsletter https://cxpassport.kit.com/signup  ✅ Bring

In Part 2 of this Think Like Amazon conversation, former Amazon Principal Engineer Steve Huynh returns to explore a different kind of leadership and leading your own life with intention. Jorge and Steve dive into what it means to truly "Think Like Amazon" beyond business: extracting full value from your environment, being curious by default, and making long-term decisions with clarity. They discuss the hidden value inside big tech jobs, the trap of living someone else's script, and how content creation can be a form of service - mentoring your younger self and others just a few steps behind you. This episode challenges you to zoom out and ask: Are you drifting, or are you designing your path? Mentioned in the episode: - Steve Huynh: https://www.linkedin.com/in/a-life-engineered/ - A Life Engineered on YouTube: https://www.youtube.com/@ALifeEngineered  

Reliable software shouldn't be an accident, but for most developers it is. Jeremy Edberg, CEO of DBOS and the guy who scaled Reddit and Netflix, joins Corey Quinn to talk about his wild idea of saving your entire app into a database so it can never really break. They chat about Jeremy's "build for three" rule, a plan for scale without going crazy, why he set Reddit's servers to Arizona time to dodge daylight saving time, and how DBOS makes your app as tough as your data. Plus, Jeremy shares his brutally honest take on distributed systems cargo cult, autonomous AI testing, and why making it easy for customers to leave actually keeps them around.Public Bio: Jeremy is an angel investor and advisor for various incubators and startups, and the CEO of DBOS. He was the founding Reliability Engineer for Netflix and before that he ran ops for reddit as its first engineering hire. Jeremy also tech-edited the highly acclaimed AWS for Dummies, and he is one of the six original AWS Heroes. He is a noted speaker in serverless computing, distributed computing, availability, rapid scaling, and cloud computing, and holds a Cognitive Science degree from UC Berkeley.Show Highlights(02:08) - What DBOS actually does(04:08) - "Everything as a database" philosophy and why it works(08:26) - "95% of people will never outgrow one Postgres machine"(10:13) - Jeremy's Arizona time zone hack at Reddit (and whether it still exists)(11:22) - "Build for three" philosophy without over-engineering(17:16) - Extracting data from mainframes older than the founders(19:00) - Autonomous testing with AI trained on your app's history(20:07) - The hardest part of dev tools(22:00) - Corey's brutal pricing page audit methodology(27:15) - Why making it easy to leave keeps customers around(34:11) - Learn more about DBOSLinksDBOS website: https://dbos.devDBOS documentation: https://docs.dbos.devDBOS GitHub: https://github.com/dbos-incDBOS Discord community: https://discord.gg/fMqo9kDJeremy Edberg on Twitter: https://x.com/jedberg?lang=enAWS Heroes program: https://aws.amazon.com/developer/community/heroes/

Join Premium! Ready for an ad-free meditation experience? Join Premium now and get every episode from ALL of our podcasts completely ad-free now! Just a few clicks makes it easy for you to listen on your favorite podcast player.  Become a PREMIUM member today by going to --> https://WomensMeditationNetwork.com/premium Breathe… Deeply, Fully. Extracting your attention from those thoughts, And bringing them here onto your breath. Following the air in through your nose, Down your chest, And deep into your belly. PAUSE… Feel your inhale expand your body, As you breathe just a bit deeper with every breath in, And extend your exhale with every breath out.  Slow it down. PAUSE… You are perfect right here. Just the way you are.  Exactly where you need to be in this moment.  Breathe, And as you breathe out, Relax your body deeper into your bed, Falling heavily into the sheets beneath you. Join our Premium Sleep for Women Channel on Apple Podcasts and get ALL 5 of our Sleep podcasts completely ad-free! Join Premium now on Apple here --> https://bit.ly/sleepforwomen  Join our Premium Meditation for Kids Channel on Apple Podcasts and get ALL 5 of our Kids podcasts completely ad-free! Join Premium now on Apple here → https://bit.ly/meditationforkidsapple Hey, I'm so glad you're taking the time to be with us today. My team and I are dedicated to making sure you have all the meditations you need throughout all the seasons of your life.  If there's a meditation you desire, but can't find, email us at Katie Krimitsos to make a request. We'd love to create what you want!  Namaste, Beautiful,

Ever wondered how to create a high-ticket offer that transforms your business and your clients' lives? Today we sit down with Austin Ford, the mastermind behind Atomic Offers, to uncover the secrets of crafting irresistible, high-value, lip licking, tasty offers.. Austin shares his journey from struggling online personal trainer to offer creation expert, including the pivotal moment when he facilitated a $300,000 sales day for a client. You'll discover why offer creation is the key to wealth creation and how to communicate the true value of your services. Don't miss this #GoldenBoulders… they will make you thousands… literally: - How to price offers for maximum profitability and client satisfaction - Understand the "anti-resentment price" concept so we don't end up hating our customers. - Discover these hidden elements that help us pick the right market - How to communicate your offer so people buy! Juicy Juicy! Plus, we'll be waiting for you at our Atomic Offers Party live on Zoom on July 10th - Spots are limited! Click here

Alan interviews Cody Soodeen. Cody Soodeen's parents lived in a town with contaminated well water - so they had to buy bottled water. This led him as an architect to invent a way, using desiccants, to extract pure water from dry air.  Today, his company, Kara Water sells air-to-water extraction units to thousands of consumers.  Make sure to subscribe to the podcast at Apple Podcasts, or wherever you get your podcasts, so you won't miss a single episode. Website: www.KaraWater.com

Extracting With pngdump.py Didier extended his pngdump.py script to make it easier to extract additional data appended to the end of the image file. https://isc.sans.edu/diary/Extracting%20With%20pngdump.py/32022 16 React Native Packages for GlueStack Backdoored Overnight 16 npm packages with over a million weekly downloads between them were compromised. The compromised packages include a remote admin tool that was seen before in similar attacks. https://www.aikido.dev/blog/supply-chain-attack-on-react-native-aria-ecosystem Atomic MacOS Stealer Exploits Clickfix MacOS users are now also targeted by fake captchas, tricking users into running exploit code. https://www.cloudsek.com/blog/amos-variant-distributed-via-clickfix-in-spectrum-themed-dynamic-delivery-campaign-by-russian-speaking-hackers Microsoft INETPUB Script Microsoft published a simple PowerShell script to restore the inetpub folder in case you removed it by mistake. https://www.powershellgallery.com/packages/Set-InetpubFolderAcl/1.0

Sun, 01 Jun 2025 21:00:00 GMT http://relay.fm/mpu/799 http://relay.fm/mpu/799 The State of Apple, with Jason Snell 799 David Sparks and Stephen Hackett Jason Snell has been covering Apple since all the Macs it shipped were beige boxes. This week, he joins Stephen and David to discuss the company's range of legal and technological issues that seem to be adding up rapidly. Jason Snell has been covering Apple since all the Macs it shipped were beige boxes. This week, he joins Stephen and David to discuss the company's range of legal and technological issues that seem to be adding up rapidly. clean 5272 Subtitle: "What Do We Have on the Spacecraft That's Good?"Jason Snell has been covering Apple since all the Macs it shipped were beige boxes. This week, he joins Stephen and David to discuss the company's range of legal and technological issues that seem to be adding up rapidly. This episode of Mac Power Users is sponsored by: Squarespace: Save 10% off your first purchase of a website or domain using code MPU. Indeed: Join more than 3.5 million businesses worldwide using Indeed to hire great talent fast. Guest Starring: Jason Snell Links and Show Notes: Sign up for the MPU email newsletter and join the MPU forums. More Power Users: Ad-free episodes with regular bonus segments Submit Feedback On Teachers and the 2025 Productivity Academy - MacSparky Join Mac Power Users - Relay Mac Power Users #462: Return to the Snell Zone - Relay Six Colors – Apple, technology, and other stuff by Jason Snell & Dan Moren Upgrade - Relay Downstream - Relay The Incomparable - Smart, funny pop culture podcasts The Incomparable Mothership Jason Snell (@jsnell@zeppelin.flights) - Mastodon Two desks, but a single M4 Max MacBook Pro – Six Colors Insta360 Link Webcam Keychron Q1 QMK Custom Mechanical Keyboard - Version 2 Extracting the Touch ID Button from a Magic Keyboard -- Friday Keyboard Club - YouTube Thunderbolt 4 Dock | TS4 | CalDigit – CalDigit Thunderbolt 5 Dock | TS5 Plus | CalDigit – CalDigit Tim Cook gambles big on the App Store and loses (for now) – Six Colors The App Store prevented more than $9 billion in fraudulent transactions - Apple App Store in the U.S. facilitated $406B in developer billings and sales in 2024 - Apple The App Store era must end, and the Mac is the model – Six Colors Safely open apps on your Mac - Apple Support How will Apple re-think AI features for WWDC 2025? – Six Colors Hypercritical: Apple Turnover Hypercritical: Apple Turnaround Apple University - Wikipedia Make | Automation Software Linode | Akamai Numbers on the Mac App Store Jason Snell on the Magic Behind the Six Colors Charts - Podfeet Podcasts Chit chat about charts - All this A full list of new functions in Numbers 14.4 – Six Colors Pushover: Simple Notifications for Android, iPhone, iPad, and Desktop Scriptable on the Mac App Store SwiftBar

As DeFi continues to evolve, the challenge of finding a balance between decentralization and protection from all manner of exploits persists. The founder of Infinex, Kain Warwick, joined the show to talk about: How crypto market makers have at times veered into “all-out crime” What market making looks like today Playing chart games with token allocations What Kain looks at when evaluating tokens Why Binance kicked a MOVE market maker off its platform The $JELLY attack on Hyperliquid and the problem of centralization in DeFi What problems in crypto Kain is attempting to solve with Infinex Visit our website for breaking news, analysis, op-eds, articles to learn about crypto, and much more: unchainedcrypto.com Thank you to our sponsors! Bitwise Guest: Kain Warwick, founder of Infinex App and Synthetix Previous appearances on Unchained: 2025 Will Be a Year of Crypto Competition. Can Ethereum Make a Comeback? Links: Crypto Market Making Kain Warwick: Discussion about market makers Binance: What happened with MOVE on Binance Coindesk: Binance Offboards Market Maker That It Said Made $38M Profit on MOVE Listing Bloomberg: Citadel Securities Plots Jump Into Crypto Trading After Trump's Embrace Hyperliquid Unchained: Hyperliquid Saved Itself a $15 Million Loss, but Sparked Criticism Infinex The Block: Synthetix founder Kain Warwick launches Infinex The Block: Peter Thiel's Founders Fund invests in Infinex's Patron NFT sale as total amount raised hits $67.7 million Timestamps:

Have you ever experienced something so painful that you just wanted to erase it from your memory forever? Those "never again" moments that feel too heavy to carry? We all have them – whether it's a relapse, a toxic relationship, or a betrayal that left us wounded.In this powerful episode of The Addicted Mind Podcast, hosts Duane and Eric explore the practice of "benefit finding" – a transformative approach to mining our painful experiences for growth and wisdom. Instead of pushing away difficult memories, they suggest we might find our greatest lessons within them.This isn't about toxic positivity or pretending everything happens for a reason. It's about recognizing our remarkable human capacity to make meaning from suffering. As Viktor Frankl discovered in the concentration camps, "suffering ceases to be suffering the moment it finds a meaning."Modern psychology calls this "post-traumatic growth" – the ability to find positive changes in five key areas: appreciation of life, relationships, new possibilities, personal strength, and spiritual change. When we intentionally reframe negative experiences, we're actually rewiring our brain through neuroplasticity.The hosts provide a practical four-step process to transform your pain into wisdom: Identify the negative experience you never want to repeat Understand why you want to avoid it Extract the valuable lessons within it Create a document of your "new learnings" Through this process, your darkest moments can become sources of inspiration and light – not just for yourself, but for others around you. As Brené Brown reminds us, "Our wholeness actually depends on the integration of all of our experiences, including the falls."Whether you're in recovery or simply navigating life's challenges, this episode offers a compassionate roadmap for turning pain into purpose. Download the accompanying worksheet to begin your journey of transformation today.Download the WorksheetKey Topics The natural tendency to want to forget painful experiences vs. the value of mining them for wisdom  Post-traumatic growth and the five areas where people can grow through difficult experiences  How neuroplasticity allows us to rewire our brains when we reframe negative experiences  The difference between benefit finding and toxic positivity • Viktor Frankl's insights on finding meaning in suffering  A practical four-step process for transforming pain into wisdom  How to create a living document of "new learnings" from painful experiences Timestamp[00:00:54] Introduction to the topic of painful experiences we wish we could erase [00:04:00] Explanation of benefit finding and transforming pain into growth [00:07:40] Discussion of Viktor Frankl and making meaning from suffering [00:08:29] The five areas of post-traumatic growth [00:09:12] How reframing negative experiences rewires our brains [00:13:00] Step 1 & 2: Identifying and understanding your painful experience [00:15:22] Steps 3 & 4: Extracting lessons and creating new learningsFollow and Review: We'd love it even more if you could drop a review or 5-star rating over on Apple Podcasts. Simply select “Ratings and Reviews” and “Write a Review” then a quick line with your favorite part of the episode. It only takes a second and it helps spread the word about the podcast.Supporting Resources:If you live in California and are looking for counseling or therapy please check out Novus Counseling and Recovery CenterNovusMindfulLife.comWe want to hear from you. Leave us a message or ask us a question: https://www.speakpipe.com/addictedmindDisclaimer Learn more about your ad choices. Visit megaphone.fm/adchoicesSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Have you ever experienced something so painful that you just wanted to erase it from your memory forever? Those "never again" moments that feel too heavy to carry? We all have them – whether it's a relapse, a toxic relationship, or a betrayal that left us wounded. In this powerful episode of The Addicted Mind Podcast, hosts Duane and Eric explore the practice of "benefit finding" – a transformative approach to mining our painful experiences for growth and wisdom. Instead of pushing away difficult memories, they suggest we might find our greatest lessons within them. This isn't about toxic positivity or pretending everything happens for a reason. It's about recognizing our remarkable human capacity to make meaning from suffering. As Viktor Frankl discovered in the concentration camps, "suffering ceases to be suffering the moment it finds a meaning." Modern psychology calls this "post-traumatic growth" – the ability to find positive changes in five key areas: appreciation of life, relationships, new possibilities, personal strength, and spiritual change. When we intentionally reframe negative experiences, we're actually rewiring our brain through neuroplasticity. The hosts provide a practical four-step process to transform your pain into wisdom: Identify the negative experience you never want to repeat Understand why you want to avoid it Extract the valuable lessons within it Create a document of your "new learnings" Through this process, your darkest moments can become sources of inspiration and light – not just for yourself, but for others around you. As Brené Brown reminds us, "Our wholeness actually depends on the integration of all of our experiences, including the falls." Whether you're in recovery or simply navigating life's challenges, this episode offers a compassionate roadmap for turning pain into purpose. Download the accompanying worksheet to begin your journey of transformation today. Download the Worksheet Key Topics The natural tendency to want to forget painful experiences vs. the value of mining them for wisdom  Post-traumatic growth and the five areas where people can grow through difficult experiences  How neuroplasticity allows us to rewire our brains when we reframe negative experiences  The difference between benefit finding and toxic positivity • Viktor Frankl's insights on finding meaning in suffering  A practical four-step process for transforming pain into wisdom  How to create a living document of "new learnings" from painful experiences Timestamp [00:00:54] Introduction to the topic of painful experiences we wish we could erase  [00:04:00] Explanation of benefit finding and transforming pain into growth  [00:07:40] Discussion of Viktor Frankl and making meaning from suffering  [00:08:29] The five areas of post-traumatic growth  [00:09:12] How reframing negative experiences rewires our brains  [00:13:00] Step 1 & 2: Identifying and understanding your painful experience  [00:15:22] Steps 3 & 4: Extracting lessons and creating new learnings Follow and Review: We'd love it even more if you could drop a review or 5-star rating over on Apple Podcasts. Simply select “Ratings and Reviews” and “Write a Review” then a quick line with your favorite part of the episode. It only takes a second and it helps spread the word about the podcast. Supporting Resources: If you live in California and are looking for counseling or therapy please check out Novus Counseling and Recovery Center NovusMindfulLife.com We want to hear from you. Leave us a message or ask us a question: https://www.speakpipe.com/addictedmind Disclaimer Learn more about your ad choices. Visit megaphone.fm/adchoices

A year and a half ago, neuroscientist Kamilla Souza got the call she'd been waiting for: A baby humpback whale had died just offshore. She wanted its brain. That's because scientists know little about the brains of whales and dolphins off the Central and South American coasts. Studying them, like Kamilla is doing, can teach scientists about the inner workings of these animals — about their behavior and how they're adapted to living underwater. So, she has to race against time to save the brains. The heat in this area of Brazil accelerates decomposition. Minutes matter. This episode was reported by Ari Daniel. Read more of Ari's reporting.Curious about other biology research happening around the world? Let us know by emailing shortwave@npr.org!Learn more about sponsor message choices: podcastchoices.com/adchoicesNPR Privacy Policy

With Ohio State being a big favorite over Notre Dame in the National Championship game, Mike and Jim navigate that game trying to find the best value. Download the latest episode of Cash the Ticket today. To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices