Podcasts about Unix

Unix Pipes under Load, Powering up a IBM Calculator from 1948, FreeBSD AI assisted Vulnerability Discovery Project, and more... NOTES This episode of BSDNow is brought to you by Tarsnap and the BSDNow Patreon Headlines Unix Pipes Under Load: Streaming, Barriers, Backpressure, and Bottlenecks Powering up a module from the IBM 604: an electronic calculator from 1948 News Roundup FreeBSD AI-assisted Vulnerability Discovery Project launch Sometimes it actually is the network: a war story FreeBSD Tribal Knowledge: Boot Enviroment Management Beastie Bits OpenSSH 10.4/10.4p1 released! OpenBSD/amd64 kernel virtual address space is now 512GB OpenBSD's pledge(2) and unveil(2) are developer-friendly, study finds Tarsnap This weeks episode of BSDNow was sponsored by our friends at Tarsnap, the only secure online backup you can trust your data to. Even paranoids need backups. Feedback/Questions Send questions, comments, show ideas/topics, or stories you want mentioned on the show to feedback@bsdnow.tv Join us and other BSD Fans in our BSD Now Telegram channel

Mozilla is paying for Firefox to be on the front of Wrexham football shirts, SCO vs IBM might not be dead after all, Red Hat will support RHEL forever if you are willing to pay, OpenMandriva discovers why distros need good governance, Félim awaits his statue, and a quick Kagi update. News Wrexham AFC and Firefox announce a multi-year, front-of-kit partnership Zombie ‘who owns Unix?' lawsuit comes alive again Red Hat Enterprise Linux Long-Life Add-On: Your path to RHEL with no pre-determined end date OpenMandriva Statement regarding attempted distribution sabotage KDE at 30 Support us on patreon and get an ad-free RSS feed with some early episodes See our contact page for ways to get in touch. RSS: Subscribe to the RSS feeds here

Mozilla is paying for Firefox to be on the front of Wrexham football shirts, SCO vs IBM might not be dead after all, Red Hat will support RHEL forever if you are willing to pay, OpenMandriva discovers why distros need good governance, Félim awaits his statue, and a quick Kagi update. News Wrexham AFC and Firefox announce a multi-year, front-of-kit partnership Zombie ‘who owns Unix?' lawsuit comes alive again Red Hat Enterprise Linux Long-Life Add-On: Your path to RHEL with no pre-determined end date OpenMandriva Statement regarding attempted distribution sabotage KDE at 30 Support us on patreon and get an ad-free RSS feed with some early episodes See our contact page for ways to get in touch. RSS: Subscribe to the RSS feeds here

Apple is suing OpenAI for allegedly stealing trade secrets, sparking a heated debate about AI, hardware ambitions, and Silicon Valley's shifting alliances. Leo, Wesley, Lou, and Patrick tackle what Apple's legal gambit could mean for OpenAI's IPO and the future of AI development. Apple Sues OpenAI for Trade Secret Theft in Blockbuster Case Apple's OpenAI lawsuit highlights broader tensions Microsoft says the world is changing faster than it can keep up as it guts commercial, Xbox teams Claude Fable 5 promotional access Meta says four states want $1.4 trillion in penalties at August youth-safety trial Meta Removes A.I. Feature on Instagram After Days of Backlash Meta tests 'super sensing' AI glasses that can capture every moment Meta Ordered by E.U. to Alter 'Addictive Design' of Instagram and Facebook The backlash against Sony ditching PlayStation discs is not slowing down Colibrì proof-of-concept gains frontier-level 1.5-TB AI model — novel approach runs on only 25GB of RAM and shows promise for local AI setups Waymo called the cops on teen riders, raising privacy concerns Waymo says some of its robotaxis ran out of power during San Francisco's July 4 gridlock and had to be towed Zombie 'who owns Unix?' lawsuit comes alive again Copy That Floppy! - Copy That Floppy! Host: Leo Laporte Guests: Wesley Faulkner, Louis Maresca, and Patrick Beja Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT joindeleteme.com/twit-biz NetSuite.AI/TWIT zscaler.com/security bitwarden.com/twit ZipRecruiter.com/twit

Apple is suing OpenAI for allegedly stealing trade secrets, sparking a heated debate about AI, hardware ambitions, and Silicon Valley's shifting alliances. Leo, Wesley, Lou, and Patrick tackle what Apple's legal gambit could mean for OpenAI's IPO and the future of AI development. Apple Sues OpenAI for Trade Secret Theft in Blockbuster Case Apple's OpenAI lawsuit highlights broader tensions Microsoft says the world is changing faster than it can keep up as it guts commercial, Xbox teams Claude Fable 5 promotional access Meta says four states want $1.4 trillion in penalties at August youth-safety trial Meta Removes A.I. Feature on Instagram After Days of Backlash Meta tests 'super sensing' AI glasses that can capture every moment Meta Ordered by E.U. to Alter 'Addictive Design' of Instagram and Facebook The backlash against Sony ditching PlayStation discs is not slowing down Colibrì proof-of-concept gains frontier-level 1.5-TB AI model — novel approach runs on only 25GB of RAM and shows promise for local AI setups Waymo called the cops on teen riders, raising privacy concerns Waymo says some of its robotaxis ran out of power during San Francisco's July 4 gridlock and had to be towed Zombie 'who owns Unix?' lawsuit comes alive again Copy That Floppy! - Copy That Floppy! Host: Leo Laporte Guests: Wesley Faulkner, Louis Maresca, and Patrick Beja Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT joindeleteme.com/twit-biz NetSuite.AI/TWIT zscaler.com/security bitwarden.com/twit ZipRecruiter.com/twit

Apple is suing OpenAI for allegedly stealing trade secrets, sparking a heated debate about AI, hardware ambitions, and Silicon Valley's shifting alliances. Leo, Wesley, Lou, and Patrick tackle what Apple's legal gambit could mean for OpenAI's IPO and the future of AI development. Apple Sues OpenAI for Trade Secret Theft in Blockbuster Case Apple's OpenAI lawsuit highlights broader tensions Microsoft says the world is changing faster than it can keep up as it guts commercial, Xbox teams Claude Fable 5 promotional access Meta says four states want $1.4 trillion in penalties at August youth-safety trial Meta Removes A.I. Feature on Instagram After Days of Backlash Meta tests 'super sensing' AI glasses that can capture every moment Meta Ordered by E.U. to Alter 'Addictive Design' of Instagram and Facebook The backlash against Sony ditching PlayStation discs is not slowing down Colibrì proof-of-concept gains frontier-level 1.5-TB AI model — novel approach runs on only 25GB of RAM and shows promise for local AI setups Waymo called the cops on teen riders, raising privacy concerns Waymo says some of its robotaxis ran out of power during San Francisco's July 4 gridlock and had to be towed Zombie 'who owns Unix?' lawsuit comes alive again Copy That Floppy! - Copy That Floppy! Host: Leo Laporte Guests: Wesley Faulkner, Louis Maresca, and Patrick Beja Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT joindeleteme.com/twit-biz NetSuite.AI/TWIT zscaler.com/security bitwarden.com/twit ZipRecruiter.com/twit

Apple is suing OpenAI for allegedly stealing trade secrets, sparking a heated debate about AI, hardware ambitions, and Silicon Valley's shifting alliances. Leo, Wesley, Lou, and Patrick tackle what Apple's legal gambit could mean for OpenAI's IPO and the future of AI development. Apple Sues OpenAI for Trade Secret Theft in Blockbuster Case Apple's OpenAI lawsuit highlights broader tensions Microsoft says the world is changing faster than it can keep up as it guts commercial, Xbox teams Claude Fable 5 promotional access Meta says four states want $1.4 trillion in penalties at August youth-safety trial Meta Removes A.I. Feature on Instagram After Days of Backlash Meta tests 'super sensing' AI glasses that can capture every moment Meta Ordered by E.U. to Alter 'Addictive Design' of Instagram and Facebook The backlash against Sony ditching PlayStation discs is not slowing down Colibrì proof-of-concept gains frontier-level 1.5-TB AI model — novel approach runs on only 25GB of RAM and shows promise for local AI setups Waymo called the cops on teen riders, raising privacy concerns Waymo says some of its robotaxis ran out of power during San Francisco's July 4 gridlock and had to be towed Zombie 'who owns Unix?' lawsuit comes alive again Copy That Floppy! - Copy That Floppy! Host: Leo Laporte Guests: Wesley Faulkner, Louis Maresca, and Patrick Beja Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT joindeleteme.com/twit-biz NetSuite.AI/TWIT zscaler.com/security bitwarden.com/twit ZipRecruiter.com/twit

Apple is suing OpenAI for allegedly stealing trade secrets, sparking a heated debate about AI, hardware ambitions, and Silicon Valley's shifting alliances. Leo, Wesley, Lou, and Patrick tackle what Apple's legal gambit could mean for OpenAI's IPO and the future of AI development. Apple Sues OpenAI for Trade Secret Theft in Blockbuster Case Apple's OpenAI lawsuit highlights broader tensions Microsoft says the world is changing faster than it can keep up as it guts commercial, Xbox teams Claude Fable 5 promotional access Meta says four states want $1.4 trillion in penalties at August youth-safety trial Meta Removes A.I. Feature on Instagram After Days of Backlash Meta tests 'super sensing' AI glasses that can capture every moment Meta Ordered by E.U. to Alter 'Addictive Design' of Instagram and Facebook The backlash against Sony ditching PlayStation discs is not slowing down Colibrì proof-of-concept gains frontier-level 1.5-TB AI model — novel approach runs on only 25GB of RAM and shows promise for local AI setups Waymo called the cops on teen riders, raising privacy concerns Waymo says some of its robotaxis ran out of power during San Francisco's July 4 gridlock and had to be towed Zombie 'who owns Unix?' lawsuit comes alive again Copy That Floppy! - Copy That Floppy! Host: Leo Laporte Guests: Wesley Faulkner, Louis Maresca, and Patrick Beja Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT joindeleteme.com/twit-biz NetSuite.AI/TWIT zscaler.com/security bitwarden.com/twit ZipRecruiter.com/twit

Apple is suing OpenAI for allegedly stealing trade secrets, sparking a heated debate about AI, hardware ambitions, and Silicon Valley's shifting alliances. Leo, Wesley, Lou, and Patrick tackle what Apple's legal gambit could mean for OpenAI's IPO and the future of AI development. Apple Sues OpenAI for Trade Secret Theft in Blockbuster Case Apple's OpenAI lawsuit highlights broader tensions Microsoft says the world is changing faster than it can keep up as it guts commercial, Xbox teams Claude Fable 5 promotional access Meta says four states want $1.4 trillion in penalties at August youth-safety trial Meta Removes A.I. Feature on Instagram After Days of Backlash Meta tests 'super sensing' AI glasses that can capture every moment Meta Ordered by E.U. to Alter 'Addictive Design' of Instagram and Facebook The backlash against Sony ditching PlayStation discs is not slowing down Colibrì proof-of-concept gains frontier-level 1.5-TB AI model — novel approach runs on only 25GB of RAM and shows promise for local AI setups Waymo called the cops on teen riders, raising privacy concerns Waymo says some of its robotaxis ran out of power during San Francisco's July 4 gridlock and had to be towed Zombie 'who owns Unix?' lawsuit comes alive again Copy That Floppy! - Copy That Floppy! Host: Leo Laporte Guests: Wesley Faulkner, Louis Maresca, and Patrick Beja Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT joindeleteme.com/twit-biz NetSuite.AI/TWIT zscaler.com/security bitwarden.com/twit ZipRecruiter.com/twit

Parce que… c'est l'épisode 0x31A! Shameless plug 19 septembre 2026 - Bsides Montréal 20 au 26 septembre 2026 - BruCON 13 novembre 2026 - DEATHCon 16 au 19 novembre - European Cyber Week 1 au 3 décembre 2026 - Forum INCYBER - Canada 2026 24 et 25 février 2027 - SéQCure 2027 Notes IA ou Ghost in the shell Secret Claude tracker shocks users after Anthropic's anti-surveillance stance Automatic Association of Cloud Security Controls and Quantifiable Metrics for Certification This work is partially supported Beyond the Syntax: Do Security Experts Trust LLMs for NIDS Rule Engineering? From Beats to Breaches: How Offensive AI Infers Sensitive User Information from Playlists “God has helped us, and so will AI”: How the Terrorist Group Boko Haram Uses Frontier AI What the New AI Executive Order Means for GovTech Hidden Web Prompts Trick AI Agents Into Sending Money Build your own vulnerability harness T3MP3ST Security Framework With 35 Tools, Turns AI Coding Agents Into 0-Day Bug Hunters US Cyber Agency Is Using Anthropic's Mythos To Audit Government Code Microsoft warns customers AI will mean busier Patch Tuesdays AI meets Cryptography 1: What AI Found in Cloudflare's CIRCL - ZK/SEC Quarterly La guerre, la guerre, c'est pas une raison pour se faire mal! Rien Souveraineté ou vive le numérique libre! Why Being Locked Out of Frontier AI is The Sovereignty Threat Canada Missed Facing US export controls, China's DeepSeek plans to make its own chips Privacy ou cachez ces informations que je ne saurais voir Footage Shows Cop Stalking Woman He Met on a TV Set After Surveilling Her With a License Plate Reader All Cars Sold in the EU Now Require a Camera Aimed at Your Face. It's Still Not Clear Where That Data Goes The Masks We (Think We) Wear: Privacy Threats of Browser-Extension Wallets in the Web3 Ecosystem Microsoft to enable Windows settings backup by default for orgs Facial Recognition in UK Shops Will Soon Instantly Alert Police About Offenders I am the law Control ton chat EU now one step away from reviving private message scanning rules Chat Control 1.0 vs 2.0 - Fight Chat Control Chat control 1.0 has renewed till 2028 Showdown in Strasbourg: The unexpected return of Chat Control 1.0 GitHub - SmtimesIWndr/gdid-reversal · GitHub Blocked Twice: How Bill C-34's Kids' Social Media Ban Would Compound the Online News Act's Harm to Young Canadians' News Access Supreme Court Allows Texas To Require Age Verification For Mobile Apps Red ou tout ce qui est brisé Google pays $250K for Linux vulnerability allowing guest VM escapes Bug in top AI coding agents shows that Unix-era security headaches never really die Software Is Now Written at the Speed of Thought. Security Isn't. Finding vulnerabilities was never the hard part Blue ou tout ce qui améliore notre posture The Cathedral and the Bazaar of Software Vulnerabilities: From the NVD to the CNAs Microsoft closes book on Nightmare Eclipse's RoguePlanet zero-day Divers ou parce que j'ai aucune idée où les placer The growing list of European organisations that ban personal messaging apps at work Vos vieux disques Mac chiffrés ont une date de péremption Collaborateurs Nicolas-Loïc Fortin Crédits Montage par Intrasecure inc Locaux réels par Intrasecure inc

The 40 Most Rage-Inducing Problems in Tech, ZFS vs Cep, Detecting and removing dangerous secrets on dev workstations before Shai-Hulud does and more... NOTES This episode of BSDNow is brought to you by Tarsnap and the BSDNow Patreon Headlines The 40 Most Rage-Inducing Problems in Tech ZFS vs Ceph: Do You Actually Need Ceph? News Roundup Detecting and removing dangerous secrets on dev workstations before Shai-Hulud does FreeBSD sh for MacOS syslogd(8) privileged and non-privileged parts now separate binaries Getting Victoria Logs running on FreeBSD Tarsnap This weeks episode of BSDNow was sponsored by our friends at Tarsnap, the only secure online backup you can trust your data to. Even paranoids need backups. Feedback/Questions Send questions, comments, show ideas/topics, or stories you want mentioned on the show to feedback@bsdnow.tv Join us and other BSD Fans in our BSD Now Telegram channel

This show has been flagged as Clean by the host. This series is dedicated to exploring little-known—and occasionally useful—trinkets lurking in the dusty corners of UNIX-like operating systems. In UNIX Curio #8 ( HPR episode 4657 ), I talked about using standard utilities to compare files. Left unmentioned, however, was a method commonly used today—the hash function. As I've stated in previous entries, while I am an engineer, I don't have a background in computer science, so my understanding of the mathematics is limited. But I can give a practical description of what a hash function does. It takes an input, performs a set of calculations on it, and produces an output. As hash functions are practically used, the input is a set of bytes, such as a file or another piece of data like a password. The output is a numerical value in a fixed range—most often, expressed as hexadecimal characters. Because this "hash value" can always be represented in a certain number of bytes, its length as printed is usually a constant number of characters, padded with leading zeros if necessary. This episode will not cover the use of hashes in programming, focusing instead on using them to validate data. A hash function, or more specifically, a cryptographic hash function, has an additional property. It should be very difficult to predict what changes to the input would be required to produce a specific change in the output. An older, related concept is called a "checksum". While these are designed to vary when the input data is damaged or digits are transposed, they do not necessarily have that last property mentioned for cryptographic hashes. You have probably already encountered a checksum, even if you didn't recognize it. On a 16-digit number assigned to a Mastercard or Visa 1 credit or debit card, the first six digits identify the card issuer (such as a bank), the next nine digits are assigned to you by the issuer, and the last digit is a check digit. The check digit is calculated using the values of the previous 15 digits, and it is a simple way to avoid typos in entering a card number. In another example, every Ethernet frame that your devices send or receive includes a checksum 2 to help ensure that the contents weren't scrambled in transit. This is 32 bits long and is called a cyclical redundancy check, commonly referred to as a CRC. A CRC is also used in many other places—for example, the .zip file format includes one for each archive member, and this allows a program extracting files from the archive to identify if any were damaged. Our UNIX Curio for today is another example, the cksum utility 3 . It generates a 32-bit CRC based on the Ethernet algorithm. It operates on either a named file or standard input and outputs the CRC value, the length of the input, and the pathname if a file was given as an argument. Unlike most modern hashing programs, the checksum is printed as a decimal integer and is not padded, so it can be anywhere from one to ten digits long. The length value is the number of bytes in the input (actually specified as the number of octets , as systems could potentially use a byte that isn't eight bits long), also expressed as a decimal integer. There are two major ways that one could use cksum to check the validity of a file. First, if you are transferring a file from one UNIX-like system to another, you could run cksum against it on both systems and check that the CRC and length are the same. The utility can also be given multiple filenames as arguments, which would generate a list that can then be compared. The second way would be for someone publishing a file or set of files to also publish the CRC values, lengths, and names so that people downloading them could verify that they match. However, I don't think the practice of publishing lists like this really started until more recent hash functions like MD5 and SHA-1 came about so it is unlikely that anyone would publish CRC values instead. The advantage of these tools should be pretty obvious in comparison to cmp , one of the utilities discussed in UNIX Curio #8. To verify a file using cmp , you need two files to compare—if you're trying to check a large file you downloaded, you would need to spend the time and bandwidth to download a second copy. And if they didn't match, you would have no idea which of the two, if either, was correct. By contrast, cksum is quicker to run, doesn't require downloading a massive amount of excess data, and if run against the original file, makes clear what the correct value is. This utility is a follow-on from a program called sum , which operated very much the same. I had a bit of trouble tracking down the exact development history, but what seems clear is that two different variants 4 were popular: a BSD version and a System V version. Both output 16-bit checksums, but used different algorithms so they didn't give the same results. Also, the BSD version printed the length of the input data as the number of 1,024-byte blocks, while the System V version instead gave a count of 512-byte blocks. (Some sources claim that System V sum generates a 32-bit checksum 5 , which could possibly be true internal to the algorithm, but I have tested several independent implementations of the utility and all of them output a 16-bit value for both the System V and BSD algorithms.) From what I can tell, the BSD version 6,7 came first; it was in 3BSD but probably appeared even earlier. An identical copy of BSD's sum was included with UNIX/32V 8,9 , which was AT&T's 1979 port of Seventh Edition UNIX to the VAX and became one of the ancestors of System III. The divergence seems to have started with System III, released in 1980; its version of the sum utility 10,11 changed to a new default algorithm, though it could be made to use the BSD algorithm via the -r option. System V looks to have kept the same behavior as System III. It's not clear to me why this algorithm is universally called the "System V algorithm" rather than the "System III algorithm"; perhaps it is because System V saw much more widespread use. Instead of trying to reconcile these differences, the POSIX committee decided to create a new utility with a unique name, use a separate algorithm entirely, and avoid the block-length dispute by printing the length in octets instead of blocks. I should point out that POSIX states that the CRC algorithm for cksum does not strictly meet the mathematical definition of a "checksum". I don't know enough to say exactly why it doesn't qualify or to say whether either of the sum algorithms do. However, in less-formal usage the term "checksum" has gathered the meaning of any value used to represent or validate a set of data, so I am fine with using it no matter the technical details of the algorithm. When two different inputs produce the same checksum or hash value, this is called a "collision". Because the output value has a limited range, there are an infinite number of possible inputs that could produce a collision. From a practical standpoint the possibilities are more limited—the majority of these inputs are larger than the number of atoms in the universe, which can't fit on any machine. Unlike a cryptographic hash algorithm, the CRC is not specifically designed to resist an attacker crafting a malicious input that would cause a collision. However, it should be sufficient to detect accidental damage. Programs implementing more modern cryptographic hash algorithms are superior to the checksum utilities in avoiding collisions (whether malicious or accidental), but there are still three advantages that the older programs have. First, a system running a historical operating system might not have the hash programs available, but is more likely to have cksum or sum already included. Second, the checksum values are much shorter than the hashes output by the newer programs, making them easier for a user to compare by looking at them. This advantage is not as great as it might appear at first, because a common way to check a hash these days is to save a list of hashes and filenames—the hash programs can use that and do the comparison themselves, sparing the user from having to validate it character by character. The third advantage is that cksum prints the input length in bytes. This greatly limits the number of inputs that could be maliciously crafted to create a collision. I did a moderate amount of research on implementations of modern cryptographic hash algorithms and found that some, such as MD5, SHA-1, and SHA-2, do use the length of the input (often termed "message length" in the literature) as part of the material fed in to the algorithm, but none of the hashing utilities present this length to the user as part of its output. There are two possible reasons for this that seem evident to me. First, if one is hashing a password, you would certainly not want to give a clear indication of its length—that would give any attacker a massive head start on guessing the password. However, that doesn't explain why one would avoid printing the input length for a file that is made publicly available. Second, it is convenient in many contexts, such as database entries or in software (such as git ), for the hash to be a fixed length. Including an extra value that can be of variable length would complicate those use cases. However, the length value could simply be dropped and they would be no worse off than they are currently. Historically on UNIX, password hashing was treated differently from checksumming files— the crypt() function 12 was used for passwords while sum and later cksum were used to confirm a file's integrity. So even rather early on, these two use cases employed algorithms with different properties, but I haven't dived into the history deeply enough to know how intentional this was. My discussion in this episode focuses on the file use case, so understand that I'm largely avoiding the topic of password hashing. Digital signatures are yet another use case, one that I'm ignoring entirely. Every few years, some security researcher declares a particular hash algorithm to be "broken" and that everyone should move over to a new one, which generally has a longer hash. While the larger hash space certainly reduces the opportunity for collisions, this disrupts workflows, such as publishing information about software releases by e-mail, which still tends to observe a 78-character limit on each line 13 , making it harder to include a list of hashes with filenames next to them. This is in addition to the work of modifying software and scripts to use the new algorithm and managing how to treat past data. It seems to me that publishing the input length along with the hash would make it far more difficult to craft a malicious input that matches both, but I haven't found discussion of that during my investigation. (See the Appendix for a possible implementation.) Perhaps someone listening can record a response episode for HPR explaining that. References: Payment card number https://en.wikipedia.org/wiki/Payment_card_number Ethernet frame: Frame check sequence https://en.wikipedia.org/wiki/Ethernet_frame#Frame_check_sequence Cksum specification https://pubs.opengroup.org/onlinepubs/009695399/utilities/cksum.html GNU coreutils manual: sum https://www.gnu.org/software/coreutils/manual/html_node/sum-invocation.html FreeBSD 15.0 sum manual page https://man.freebsd.org/cgi/man.cgi?query=sum&sektion=1&manpath=FreeBSD+15.0-RELEASE+and+Ports 3BSD sum manual page https://www.tuhs.org/cgi-bin/utree.pl?file=3BSD/usr/man/man1/sum.1 3BSD sum source https://www.tuhs.org/cgi-bin/utree.pl?file=3BSD/usr/src/cmd/sum.c UNIX/32V sum manual page https://www.tuhs.org/cgi-bin/utree.pl?file=32V/usr/man/man1/sum.1 UNIX/32V sum source https://www.tuhs.org/cgi-bin/utree.pl?file=32V/usr/src/cmd/sum.c System III sum manual page https://www.tuhs.org/cgi-bin/utree.pl?file=SysIII/usr/src/man/man1/sum.1 System III sum source https://www.tuhs.org/cgi-bin/utree.pl?file=SysIII/usr/src/cmd/sum.c Crypt specification https://pubs.opengroup.org/onlinepubs/009695399/functions/crypt.html RFC 2822: Internet Message Format: Line Length Limits https://datatracker.ietf.org/doc/html/rfc2822#section-2.1.1 OpenSSH 10.1 released https://lwn.net/ml/all/dd12623ae86aa5eb@cvs.openbsd.org/ Appendix The MD5 hash algorithm was (and still is) widely used, but many people characterize it as being "broken" and discourage its use. Let us imagine a variant of this, called MD5.L, where the normal MD5 hash is followed by a "." character and the input length expressed as a hexadecimal number. Take, for example, the e-mail message announcing the release of OpenSSH 10.1 14 . At the bottom, it includes an SHA-1 hash and an SHA-2 256-bit hash for the available gzipped tar files. That longer hash is encoded with Base64 because if it were given as a hexadecimal number, it would make the line longer than 78 bytes. The MD5.L hash of the file would be one character shorter than the SHA-1 hash, as shown below. (The extra length of the name makes them both consume the same number of characters. The hashes shown are for the "portable" version of OpenSSH.) Some people claim SHA-1 is also broken, seeking to have people use newer and longer hash functions. For an attacker to compromise MD5.L in this example, they would not only have to create a valid tar file compressed with gzip containing a malicious payload having the right MD5 hash, that file would have to be exactly 1,972,831 bytes long (the decimal equivalent of 1e1a5f). While there are still many possible inputs that could be tried (256 1972831 , to be exact*), this is far fewer than the infinite possibilities for plain MD5, SHA-1, or SHA-2. If for some reason it is super important to have a fixed hash length, let's imagine another variation called MD5+L. In this one, instead of L being the input length, it is the input length modulo one terabyte (2 40 bytes), which can be represented by 10 hexadecimal characters, left-padded with zeros. While this approach substantially increases the number of possible inputs an attacker could try, it is likely that an intended victim would notice that the file they downloaded is larger (or smaller) than expected by that much. The MD5+L hash is longer than a SHA-1 hash, but still shorter than a 256-bit SHA-2 hash. SHA1 (openssh-10.1p1.tar.gz) = 7fd17b99d1beffb47cd380d64079e920bb0bd91f SHA256 (openssh-10.1p1.tar.gz) = ufx6K4JXlGem8vQ+SoHI4d/aYU3bT5slWq/XAgu/B1g= MD5.L (openssh-10.1p1.tar.gz) = 80dd9bb00a86519934710d05903fdf07.1e1a5f MD5+L (openssh-10.1p1.tar.gz) = 80dd9bb00a86519934710d05903fdf07+00001e1a5f Of course, if MD5 is considered to be too weak even with the inclusion of the length, one could produce a ".L" or "+L" version of any hash function. However, longer hashes will end up running into the 78-character limit. *This is a number with 4.75 million digits that the bc utility on my laptop took almost 5 minutes to calculate. Provide feedback on this episode.

De alertas de segurança inusitados ao esporte do futuro. Neste episódio do FIAP Decode, André David, Silvia Kavabata e Pedro Ribeiro decodificam o caso do hacker "Misantropia" que deu um susto no Brasil inteiro, os segredos tecnológicos da bola da Copa do Mundo e os bastidores dos agentes autônomos no FIFA AI Pro.   Decodifique novas conexões - André David: Linkedin e Instagram Silvia Kavabata: Linkedin e Instagram- Pedro Ribeiro: Linkedin Conheça os projetos dos nossos decoders: WoMakersCodeCodeByGirls Azion | YouTubeNOTÍCIAS: “Misantropia”: suposto hacker diz que usou senhas vazadas de servidores públicos para enviar alertas  Terremoto na Venezuela: como celulares conseguiram alertar usuários antes dos tremores Bola da Copa: como a Adidas emprega tecnologia no Mundial?  Fifa AI Pro: como funciona o sistema de agentes da Copa?Como a Pixar recuperou Toy Story 2 depois de um comando Unix apagar o filme todo em 1998

Justin Wade is the new chair of ACT-IAC Voyagers' 2027 Professional Development Program and CTO of the Interior Business Center at the U.S. Department of the Interior. We talk about why this is a critical moment to develop agile leaders amid rapid technology shifts and increased pressure for efficiency. Wade shares his lifelong path in IT, from learning programming in first grade, studying computer science at CU Denver, early leadership as a Unix system administrator, entrepreneurial work with venture capital-backed startups, and then federal roles including Associate CIO for the Office of the Secretary, highlighting lessons in resilience, balancing technical depth with business strategy, and leading through uncertainty.Agile Product Discovery Course | ACT-IAC Summary - A Hole in One with ACT-IACSubscribe on your favorite podcast platform to never miss an episode! For more from ACT-IAC, follow us on LinkedIn or visit http://www.actiac.org.Learn more about membership at https://www.actiac.org/join.Donate to ACT-IAC at https://actiac.org/donate. Intro/Outro Music: See a Brighter Day/Gloria TellsCourtesy of Epidemic Sound(Episodes 1-159: Intro/Outro Music: Focal Point/Young CommunityCourtesy of Epidemic Sound)

inotify in FreeBSD, how changes to poudriere.conf affect the build time, Migrating mail servers from exim to OpenSMTPD, and more... NOTES This episode of BSDNow is brought to you by Tarsnap and the BSDNow Patreon Headlines Native inotify in FreeBSD News Roundup How changes to poudriere.conf affect the build time Follow on Giving poudriere a jump start Migrating mail servers from exim to OpenSMTPD (smtpd) is fun and useful Orion PDA Recap of the April 2026 Frankfurt Area FreeBSD Hackathon – Sven Ruediger Tarsnap This weeks episode of BSDNow was sponsored by our friends at Tarsnap, the only secure online backup you can trust your data to. Even paranoids need backups. Feedback/Questions Send questions, comments, show ideas/topics, or stories you want mentioned on the show to feedback@bsdnow.tv Join us and other BSD Fans in our BSD Now Telegram channel

En este episodio vamos a hablar de una de esas herramientas míticas del ecosistema Linux y Unix que prácticamente todos hemos configurado alguna vez: Cron. Ese servicio fiel, un clásico entre los clásicos, que lleva décadas ejecutando nuestras copias de seguridad de madrugada o eliminando ficheros temporales. Sin embargo, las cosas cambian, la tecnología avanza y yo creo que ha llegado el momento de que todos jubilemos a Cron. Sí, como lo oyes. Ha llegado la hora de darle una merecida jubilación dorada y abrir los brazos a una alternativa mucho más moderna, integrada y potente: los Systemd Timers.¿Por qué deberías jubilar a tu viejo Cron?Sé que puedes estar pensando: "Lorenzo, pero si a mí Cron me funciona de maravilla". Y es verdad, para un comando sencillo que se ejecute cada hora, Cron cumple. Pero a poco que intentes complicar la tarea, empiezan los problemas. El gran drama de Cron es que trabaja a ciegas y en absoluto silencio. Si tu script falla por falta de internet, por un error de permisos o porque un recurso no está disponible, no te vas a enterar a menos que te hayas tomado el trabajo de programar tus propios registros de log, gestionar lógicas de reintentos o configurar desvíos de errores dentro de tu script.El poder de los Systemd TimersCon los Systemd Timers todo esto se soluciona de forma completamente automática y sin añadir complejidad a tus scripts. Systemd se encarga de gestionar de manera integrada el estado de tu sistema y te ofrece superpoderes como:Logs centralizados automáticosGestión inteligente de la persistenciaControl de dependenciasAleatorización horariaLa anatomía de una tarea en SystemdPara conseguir toda esta potencia, Systemd utiliza un enfoque muy limpio en el que dividimos la tarea en dos archivos de texto sencillos que se complementan a la perfección:El Servicio (.service)El Timer (.timer)Automatización sin root: Los timers de usuarioPero mi funcionalidad favorita, y la que utilizo en mi día a día para casi todo, es la posibilidad de ejecutar estos temporizadores en el espacio del usuario corriente, sin necesidad de tener privilegios de administrador ni usar el comando sudo. Estos temporizadores se guardan en tu propia carpeta de configuración personal de forma limpísima y se ejecutan dentro del contexto de tu sesión activa.Capítulos del audio00:00:00 Introducción y el adiós definitivo a Cron00:01:43 Los fallos silenciosos de Cron: Logs, reintentos y dependencias00:03:06 Las grandes ventajas de usar Systemd Timers00:05:21 La anatomía de la automatización: Timer y Servicio00:06:48 Configuración de la sección [Timer], OnCalendar y persistencia00:07:55 Tareas relativas: OnBootSec y aleatorización de tiempos00:10:00 Comandos de systemctl para gestionar tus tareas programadas00:10:33 Ejemplos prácticos en el sistema: Backups y limpiezas00:12:13 Notificaciones de escritorio e integración con el entorno gráfico00:14:11 Timers de usuario: Automatización segura sin usar root o sudo00:15:25 El truco de Linger para mantener tareas activas en VPS00:16:53 Sincronización continua de notas y cambio automático de fondo00:20:07 Cómo ver los logs y depurar fallos de forma sencilla con journalctl00:21:25 Evita estos errores típicos y valida con systemd-analyze00:24:51 El futuro de la automatización, modelos de lenguaje y despedidaMás información y enlaces en las notas del episodio

This week on AwesomeCast, Sorg is joined by Dave Podnar while Katie is on assignment. The crew digs into practical tech, gaming platforms, open-source operating systems, sports broadcast innovation, retro-inspired phones, and one very funny automated social media mistake involving DoorDash, soccer, and T-Pain. Stories and gadgets discussed: Dave's Awesome Thing of the Week: Shokz OpenRun Pro 2 headphones Dave finally upgrades from cheap running headphones to the Shokz OpenRun Pro 2, praising the open-ear / bone-conduction-style design for running safety, comfort, sweat management, USB-C charging, and situational awareness. Link: https://www.amazon.com/dp/B0D2HKCMBP?maas=maas_adg_api_582366218564361552_static_9_129&ref_=aa_maas&tag=maas&aa_campaignid=lv_IGvrl5TTBxiweexlXK&aa_adgroupid=lv_fdLHIcjvJhpJZvQxFb&aa_creativeid=lv_MMLFRb8MTdZvGlnqFv&gad_campaignid=23921418215&gbraid=0AAAABDw_RJg26Vr6MY-sVAPsmwCxwK4mX&gclid=CjwKCAjw3ejRBhAdEiwADkqPn36qoEfzRElmQ3e9OXSxZdGKjw0DyckEdeqo82D8qqlMdTFDyLXuXhoCVn8QAvD_BwE&th=1 Valve opens the door to SteamOS installs on AMD PCs Sorg talks about Valve allowing SteamOS installs on normal PCs with AMD GPUs, giving PC gamers a way to build their own living-room Steam Machine instead of buying Valve's new box. The conversation also touches on Steam Deck, Linux gaming, Proton, GPU costs, tariffs, and the economics of gaming hardware. Link: https://www.pcgamer.com/hardware/steam-machines/valve-greenlights-steamos-installs-on-normal-pcs-with-amd-gpus-so-you-can-go-make-your-own-steam-machine-if-you-dont-wanna-fork-over-usd1-049/?brid=YWdncwFV2uAIYy2jMECl21FxWfGS Bazzite as an open-source SteamOS-style alternative Sorg brings up Brother Sorg's recommendation of Bazzite, a Linux-based gaming OS with Steam gaming mode and support for launchers like Xbox Game Pass, Battle.net, EA, Epic Games Store, GOG, Rockstar, and Ubisoft Connect. Link: https://bazzite.gg/ Pride Month tech history: Jon “maddog” Hall Dave spotlights Jon “maddog” Hall, a major Linux and Unix figure, sharing stories about Hall's long programming history, his connection to Linus Torvalds, and the importance of recognizing LGBTQ contributors in technology history. Article: https://www.lpi.org/blog/2025/09/10/a-lifetime-in-code-jon-maddog-hall-reflects-at-linuxfest-northwest/ Talk: https://www.youtube.com/watch?v=758QuvXrttM Chachi Says Video Game Minute: GTA 6, Nex Playground, and Ubisoft news Chachi covers three gaming stories: GTA 6 cover art and pre-orders, the Nex Playground motion-based console for kids, and the death of Claude Guillemot, co-founder of Ubisoft. GTA 6: https://www.ign.com/articles/gta-6-cover-artwork-revealed-pre-orders-begin-june-25 Nex Playground: https://www.bbc.com/news/articles/czx50rrz7zro Claude Guillemot: https://apnews.com/article/france-assassins-creed-ubisoft-plane-crash-2df2ea469c3fca0a45c38ae8805a6033 Baja SAE filmed on a “gas station camera” Sorg highlights a low-fi, retro-looking Baja SAE reel from Fairfield University, celebrating the charm of VHS-style and “bad camera” aesthetics in modern social media content. Link: https://www.instagram.com/reels/DZ6BjfCtzo-/ World Cup referee technology, digital twins, sensors, and 3D body scans Sorg discusses advanced soccer officiating tech, including camera sensors, player tracking, digital twins, and automated offside detection that can help reduce blown calls and give officials faster alerts. Link: https://apple.news/ABeSFqwyjRpehEy0x8Nc9nQ Ribbie turns MLB games into pixel-art broadcasts Dave shares Ribbie, a fan-built project that uses real-time Major League Baseball data to recreate live games as a retro 16-bit-style baseball broadcast. The conversation connects it to vibe coding, AI-assisted development, and new ways fans can build creative sports experiences. Link: https://techcrunch.com/2026/06/23/ribbie-turns-real-time-baseball-stats-into-arcade-like-pixel-art-broadcasts/ Commodore phone brings retro branding to a distraction-light flip phone Dave and Sorg look at the Commodore phone, a Sailfish OS-based flip phone designed as a step above a dumbphone, with calls, texting, maps, music apps, Uber/Lyft, classic Commodore games, and limited/no social media access. Link: https://order.commodore.net/callback-audio/?brid=YWdncwHwIXnsUg8Nsyh9q_45Lj2C DoorDash accidentally tags T-Pain in soccer posts The episode wraps with a funny social media fail where DoorDash appears to tag T-Pain instead of soccer player Tim Payne during World Cup-related posts. Sorg and Dave discuss automation, agency workflows, social scheduling, and why T-Pain's response made the whole situation better. Link: https://www.instagram.com/p/DZ56tKCnedX/?brid=YWdncwG5YHkt5InxWn1IZ1Apa3Gc&img_index=2

This week on AwesomeCast, Sorg is joined by Dave Podnar while Katie is on assignment. The crew digs into practical tech, gaming platforms, open-source operating systems, sports broadcast innovation, retro-inspired phones, and one very funny automated social media mistake involving DoorDash, soccer, and T-Pain. Stories and gadgets discussed: Dave's Awesome Thing of the Week: Shokz OpenRun Pro 2 headphones Dave finally upgrades from cheap running headphones to the Shokz OpenRun Pro 2, praising the open-ear / bone-conduction-style design for running safety, comfort, sweat management, USB-C charging, and situational awareness. Link: https://www.amazon.com/dp/B0D2HKCMBP?maas=maas_adg_api_582366218564361552_static_9_129&ref_=aa_maas&tag=maas&aa_campaignid=lv_IGvrl5TTBxiweexlXK&aa_adgroupid=lv_fdLHIcjvJhpJZvQxFb&aa_creativeid=lv_MMLFRb8MTdZvGlnqFv&gad_campaignid=23921418215&gbraid=0AAAABDw_RJg26Vr6MY-sVAPsmwCxwK4mX&gclid=CjwKCAjw3ejRBhAdEiwADkqPn36qoEfzRElmQ3e9OXSxZdGKjw0DyckEdeqo82D8qqlMdTFDyLXuXhoCVn8QAvD_BwE&th=1 Valve opens the door to SteamOS installs on AMD PCs Sorg talks about Valve allowing SteamOS installs on normal PCs with AMD GPUs, giving PC gamers a way to build their own living-room Steam Machine instead of buying Valve's new box. The conversation also touches on Steam Deck, Linux gaming, Proton, GPU costs, tariffs, and the economics of gaming hardware. Link: https://www.pcgamer.com/hardware/steam-machines/valve-greenlights-steamos-installs-on-normal-pcs-with-amd-gpus-so-you-can-go-make-your-own-steam-machine-if-you-dont-wanna-fork-over-usd1-049/?brid=YWdncwFV2uAIYy2jMECl21FxWfGS Bazzite as an open-source SteamOS-style alternative Sorg brings up Brother Sorg's recommendation of Bazzite, a Linux-based gaming OS with Steam gaming mode and support for launchers like Xbox Game Pass, Battle.net, EA, Epic Games Store, GOG, Rockstar, and Ubisoft Connect. Link: https://bazzite.gg/ Pride Month tech history: Jon “maddog” Hall Dave spotlights Jon “maddog” Hall, a major Linux and Unix figure, sharing stories about Hall's long programming history, his connection to Linus Torvalds, and the importance of recognizing LGBTQ contributors in technology history. Article: https://www.lpi.org/blog/2025/09/10/a-lifetime-in-code-jon-maddog-hall-reflects-at-linuxfest-northwest/ Talk: https://www.youtube.com/watch?v=758QuvXrttM Chachi Says Video Game Minute: GTA 6, Nex Playground, and Ubisoft news Chachi covers three gaming stories: GTA 6 cover art and pre-orders, the Nex Playground motion-based console for kids, and the death of Claude Guillemot, co-founder of Ubisoft. GTA 6: https://www.ign.com/articles/gta-6-cover-artwork-revealed-pre-orders-begin-june-25 Nex Playground: https://www.bbc.com/news/articles/czx50rrz7zro Claude Guillemot: https://apnews.com/article/france-assassins-creed-ubisoft-plane-crash-2df2ea469c3fca0a45c38ae8805a6033 Baja SAE filmed on a “gas station camera” Sorg highlights a low-fi, retro-looking Baja SAE reel from Fairfield University, celebrating the charm of VHS-style and “bad camera” aesthetics in modern social media content. Link: https://www.instagram.com/reels/DZ6BjfCtzo-/ World Cup referee technology, digital twins, sensors, and 3D body scans Sorg discusses advanced soccer officiating tech, including camera sensors, player tracking, digital twins, and automated offside detection that can help reduce blown calls and give officials faster alerts. Link: https://apple.news/ABeSFqwyjRpehEy0x8Nc9nQ Ribbie turns MLB games into pixel-art broadcasts Dave shares Ribbie, a fan-built project that uses real-time Major League Baseball data to recreate live games as a retro 16-bit-style baseball broadcast. The conversation connects it to vibe coding, AI-assisted development, and new ways fans can build creative sports experiences. Link: https://techcrunch.com/2026/06/23/ribbie-turns-real-time-baseball-stats-into-arcade-like-pixel-art-broadcasts/ Commodore phone brings retro branding to a distraction-light flip phone Dave and Sorg look at the Commodore phone, a Sailfish OS-based flip phone designed as a step above a dumbphone, with calls, texting, maps, music apps, Uber/Lyft, classic Commodore games, and limited/no social media access. Link: https://order.commodore.net/callback-audio/?brid=YWdncwHwIXnsUg8Nsyh9q_45Lj2C DoorDash accidentally tags T-Pain in soccer posts The episode wraps with a funny social media fail where DoorDash appears to tag T-Pain instead of soccer player Tim Payne during World Cup-related posts. Sorg and Dave discuss automation, agency workflows, social scheduling, and why T-Pain's response made the whole situation better. Link: https://www.instagram.com/p/DZ56tKCnedX/?brid=YWdncwG5YHkt5InxWn1IZ1Apa3Gc&img_index=2

This show has been flagged as Clean by the host. This series is dedicated to exploring little-known—and occasionally useful—trinkets lurking in the dusty corners of UNIX-like operating systems. The echo command is very useful—it prints the arguments given to it, followed by a newline character. (The newline is sometimes also called a linefeed character depending on who is writing or speaking, and has the ASCII decimal value 10.) It has many uses, either in a script or interactively on the command line. The echo utility is used to display text, the value of a variable, or the result of a pathname expansion. It can also feed text to another command in a pipeline. As useful as echo is, it should come as no surprise that it first appeared early on in Bell Laboratories' Second Edition UNIX 1 in 1972. This initial version accepted no options 2 —although the manual page doesn't explicitly say output is followed by a newline character, the description of writing "as a line" seems to imply it. In Seventh Edition UNIX, the manual page 3 makes that clear, and also features the addition of the -n option, which causes echo to print the arguments without a trailing newline character. Eighth Edition UNIX's echo 4 gained the -e option, which allows certain escape codes from the C programming language to be used. These variations caused differences in behavior between different versions of echo . Will running echo -n something on your system output the text "something" without a newline, or "-n something" followed by a newline? Things get even trickier when the command arguments include parameter or pathname expansions. If there are files named "-n" and "something" in the current directory, what does echo * output? Like the previous question, that depends on whether or not your version of echo treats -n as an option. You can't get around this ambiguity by quoting or escaping the "*", because that just causes echo to print a literal asterisk. Example using GNU utilities on Debian 12; both the "echo" utility and the "echo" builtin of bash recognize "-n" as an option. $ ls -1 -n something $ echo * something$ #Shell prompt is on the same line because "-n" was treated as an option to echo $ echo "*" * The solution was to create a new utility, which is the first UNIX Curio for today: printf . This command allows a user to print text similar to the way the identically-named function works in the C programming language. You run printf 5 followed by a format string, followed by zero or more arguments. No newline characters are printed unless specifically indicated by the format string or the arguments. To use printf to print "something" without a newline, that would just be printf something . This demonstrates that you don't need any arguments—in this example, the format string is just a set of regular characters to be displayed. If you wanted a newline character at the end, printf "somethingn" would give you that. (In this case, the format string needs to be quoted so the "n" isn't interpreted by the shell.) In addition to "n" for a newline, you can also use "a" for an alert (rings the terminal bell), "b" for a backspace, "f" for a formfeed, "r" for a carriage return, "t" for a horizontal tab, "v" for a vertical tab, and "" to get a literal backslash. In addition to these special characters, any arbitrary byte can be included using a backslash followed by one to three octal digits; however, it might be difficult to predict what will be output because it can differ based on the character set the terminal is using. It is safer and more portable to stick to the pre-defined characters if possible. The real magic of the printf utility comes from using "conversion specifications" in the format string. Probably the simplest of these to explain is the "%s" conversion specification—it represents a string of any length. The command printf "Hi, %s, how are you?n" followed by a list of names would print the greeting for each name, putting it in the place occupied by the "%s". $ printf "Hi, %s, how are you?n" Alice Bob Carol Hi, Alice, how are you? Hi, Bob, how are you? Hi, Carol, how are you? The format string is reused as many times as needed to consume all of the arguments. Take, for example, the command printf "Hi, %s, have you met %s?n" . If this is run with two name arguments, it would print the sentence on one line, using both names. If run with four name arguments, it would print the sentence twice, once with the first two names and again with the second two names. If you only gave it three names, the last "%s" conversion specification would be replaced with a null string. $ printf "Hi, %s, have you met %s?n" Alice Bob Hi, Alice, have you met Bob? $ printf "Hi, %s, have you met %s?n" Alice Bob Carol David Hi, Alice, have you met Bob? Hi, Carol, have you met David? $ printf "Hi, %s, have you met %s?n" Alice Bob Carol Hi, Alice, have you met Bob? Hi, Carol, have you met ? Three other items can also be given in each conversion specification: flags, the field width, and the precision. The exact meanings of these depend on which type of conversion specifier character you are using. For "%s", using a "-" as the flag causes the text to be left-justified instead of the default right-justified, a field width causes the printed field to be at least as long as the number given, and a precision limits the number of bytes written from the string to the number given. $ #Example of %s with a precision value $ printf "Hi, %.3s, how are you?n" Alice Bob Carol Hi, Ali, how are you? Hi, Bob, how are you? Hi, Car, how are you? $ #Example of %s with a field width $ printf "Hi, %8s, how are you?n" Alice Bob Carol Hi, Alice, how are you? Hi, Bob, how are you? Hi, Carol, how are you? $ #Example of %s with a left-justify flag and a field width $ printf "Hi, %-8s, how are you?n" Alice Bob Carol Hi, Alice , how are you? Hi, Bob , how are you? Hi, Carol , how are you? $ #Example of %s with a left-justify flag, a field width, and a precision $ printf "Hi, %-8.3s, how are you?n" Alice Bob Carol Hi, Ali , how are you? Hi, Bob , how are you? Hi, Car , how are you? While "%s" is probably the most commonly-used conversion specification, others are available. A whole set of them are dedicated to printing integer values as a signed decimal, an unsigned decimal, an unsigned octal, or an unsigned hexadecimal number. These also can take flags, a field width, and a precision. I think the details and nuances of all this are too complex to clearly explain here, so I will just refer you to the POSIX "file format notation" specification 6 . Be aware that unlike the printf function in the C programming language, the printf utility is not obligated to accept conversion specifications for floating-point numbers. While some implementations might support this, scripts intended to be portable should limit themselves to the restricted set required by the POSIX standard (%d, %i, %o, %u, %x, %X, %c, and %s, plus %b and %% described below). Two more conversion specifications are worth mentioning. The first is only required by the standard for the printf utility, not the C function, and is "%b". This is the same as "%s", except that certain backslash escape sequences in the argument will be treated specially. This includes all the ones described above except for the one using octal digits to represent a byte. In an argument, this is instead represented by "" followed by one to three octal digits. An additional backslash escape sequence accepted is "c"—this does not print anything itself, but causes printf to immediately halt output. The final conversion specification is "%%", which just outputs a literal "%". You can't use a bare "%" in the format string, because printf expects that to introduce a conversion specification. Be careful not to be tripped up by this when trying to print some value as a percentage. Example assuming that the hypothetical "/dev/batterycharge" file on your laptop outputs the battery charge level (42% in this case). As you can see, in some cases an error message might be displayed, but in others it might just behave in a way you didn't intend without complaining. GNU's "printf" utility and the "printf" builtin of bash both support "%e" as a conversion specification as an extension to POSIX. $ cat /dev/batterycharge 42 $ #Wrong $ printf "Your laptop's charge level is $(cat /dev/batterycharge)%.n" bash: printf: `': invalid format character Your laptop's charge level is 42$ #Shell prompt appears here from the error $ #Right $ printf "Your laptop's charge level is $(cat /dev/batterycharge)%%.n" Your laptop's charge level is 42%. $ #Next one treats %e as the specifier, with the space and "l" as flags $ printf "Your laptop has $(cat /dev/batterycharge)% level of charge.n" Your laptop has 42 0.000000e+00vel of charge. $ #Because no arguments were given, "0" was used for the value to convert Let's go back to the situation I was describing with echo —we have files named "-n" and "something" in the current directory and want to print all their names, separated by spaces. We could do that with printf "%s " * , which would not treat the "-n" as an option. However, the output might look a little weird because there wouldn't be a newline character at the end. We could insert a newline by using "%b" instead of "%s" and following the asterisk with a "nc" as the second argument. The "c" is there to prevent the final space in the format string from being printed after the newline. $ ls -1 -n something $ printf "%s " * -n something $ #No newline was printed here $ printf "%b " * "n" -n something $ #There's a newline, but also a spurious space before the shell prompt $ printf "%b " * "nc" -n something $ #No space before the shell prompt this time Using the "%b" conversion specification can therefore solve one problem, but it also introduces another. Arguments which include a backslash can be interpreted as escape sequences, and many systems are fine with allowing backslashes in filenames. In cases where you're just using the printf utility to display text, it's usually not a big deal if the output looks a little wonky. Where you really need to be careful is when the text is being piped to another program, as control characters and other oddities might cause unexpected results, and can potentially create security problems if processed by a script or utility running as a privileged user. $ #GNU "ls" displays filenames containing a backslash in single quotes $ ls -1 apple banana 'cherry' durian $ printf "%b " * "nc" apple banana $ #"c" in "cherry" stops output immediately The printf utility looks to have shown up first in 1986's Ninth Edition UNIX 7 , though the earliest manual page I could find 8 is from the Tenth Edition. Its first appearance in BSD seems to be from 1990 in the 4.3 Reno release 9 . Two years later, it was added to Issue 4 of The Open Group's CAE Specification. From what I can tell, it did not seem to be in AT&T's System III—presumably the printf utility did make it into System V at some point but I found it difficult to track this down. While echo is still suitable for use where you know for certain that you want a newline character printed at the end and none of the arguments will start with a hyphen, consider using the printf utility instead for displaying text. It offers more flexibility and features than you are guaranteed to get with echo , although it does require a bit of forethought in constructing a proper format string and arguments. That is not necessarily a bad thing, because a script's author should be thinking about what might happen if it is called with "strange" text or filenames. This episode also provides a good case for being careful when naming files—many filesystems will allow you to use hyphens, control characters, quotation marks, and potentially any character other than a slash or a null byte in a filename. As we've seen, some of these characters can create problems for standard utilities. While it can feel limiting, especially for people not using English, the safest filenames to use on a UNIX-like system consist only of characters in the "portable filename character set" as defined by POSIX 10 and where the first character is not a hyphen. This set includes the lowercase and uppercase letters "a" through "z", the numerals "0" through "9", and the period, underscore, and hyphen. Notably, it does not include the space character. That leads me to another UNIX Curio that I only just now discovered while researching this episode. This is the pathchk utility 11 . It can be run with one or more strings as arguments, checks each one against a set of rules for pathnames, and outputs an error message for each problem found. By default, it checks against the following limits on the system where it's being run: maximum number of bytes in the full path, maximum number of bytes in any component of the path, all byte sequences must be valid in the given directory, and the user running the program must have access to all directories referenced. If run with the -p option, instead of those limits, it checks against POSIX limits: a maximum of 256 bytes in the full path, a maximum of 14 bytes in each component of the path, and each component must only include characters from the portable set. The -P option adds warnings if any component starts with a "-" or if the pathname is completely empty. While the exit status will tell you if the checks succeeded or not, I don't feel like the pathchk utility is well suited to be used in an automated fashion, as the exact wording of its output is not specified and checks cannot be selected individually. However, it can be used interactively to validate pathnames you aren't sure about. See the linked specification for full details. References: A Research UNIX Reader: Combined Tables of Contents https://archive.org/details/a_research_unix_reader/page/n99/mode/1up A Research UNIX Reader: Second Edition UNIX echo manual page (although this page has "v1" typed at the top, the date and the tables of contents indicate it first appeared in v2, a.k.a. Second Edition) https://archive.org/details/a_research_unix_reader/page/n22/mode/1up Seventh Edition UNIX echo manual page https://man.cat-v.org/unix_7th/1/echo Eighth Edition UNIX echo manual page https://man.cat-v.org/unix_8th/1/echo Printf specification https://pubs.opengroup.org/onlinepubs/9699919799/utilities/printf.html File Format Notation specification https://pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap05.html A Research UNIX Reader: Ninth Edition Table of Contents https://archive.org/details/a_research_unix_reader/page/n95/mode/1up Tenth Edition UNIX echo/printf manual page https://man.cat-v.org/unix_10th/1/echo 4.3BSD Reno printf manual page https://man.freebsd.org/cgi/man.cgi?query=printf&sektion=1&manpath=4.3BSD+Reno Definitions: Portable Filename Character Set https://pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap03.html#tag_03_282 Pathchk specification https://pubs.opengroup.org/onlinepubs/9699919799/utilities/pathchk.html Provide feedback on this episode.

FreeBSD to OpenBSD Wireguard, Object storage with OpenZFS and SeaweedFS, a zfs script for labeling drives, and more... NOTES This episode of BSDNow is brought to you by Tarsnap and the BSDNow Patreon Headlines FreeBSD to OpenBSD Wireguard Using Object Storage with OpenZFS and SeaweedFS News Roundup zfs – a helper script for labelling all those drives AI errno(2) values The vi Family Creating a Samba Active Directory Domain Controller on FreeBSD Beastie Bits Let's find out how to get predictable IPv6 addresses assigned to OpenBSD VMs Tarsnap This weeks episode of BSDNow was sponsored by our friends at Tarsnap, the only secure online backup you can trust your data to. Even paranoids need backups. Feedback/Questions Davi - BSDCan 2026 Send questions, comments, show ideas/topics, or stories you want mentioned on the show to feedback@bsdnow.tv Join us and other BSD Fans in our BSD Now Telegram channel

A version of this essay has been published by Open Magazine at https://openthemagazine.com/world/india-will-collapse-without-digital-sovereignty-and-pax-indica-lessons-from-hormuzBy now it is clear that the Iran War (or West Asia War) has been a disaster to all concerned, including the principals as well as assorted passersby. The massive amounts spent by the US (at last count $25 billion) are at least articulated; the bill for the enormous infrastructural and human suffering inflicted on Gulf states, in the theater of war, must be greater, by definition.The collateral damages suffered by the rest of the world from the cessation of trade through the Straits of Hormuz will presumably run into the trillions of dollars. As one of the worst affected, India, which imports 90% of its hydrocarbons from the Gulf, not to mention other essential items such as urea (for fertilizer), sulfuric acid, helium, etc., is on track to take a massive hit. As an article in The Economic Times said, “India must brace for broad-based economic shock”.Indian exports of up to $50 billion are also affected, especially agricultural products including perishable foodstuffs, but also gems and jewellery, electronics, textiles and garments. Some of this can be diverted via Oman and the UAE's Fujairah port, but much of it passes through the Straits of Hormuz and is potentially blocked and/or stranded at sea.The Hormuz closure is a body blow to India's economy. What can and will India do about it? The Indian State has a habit of rising to the challenge only when there is a crisis, while vegetating otherwise. The 1991 economic crisis is a case in point; the sanctions following “The Buddha is smiling”, and the denial of cryogenic rocket engines and supercomputers are other examples where the nation rallied. So were covid vaccines. Necessity, they say, is the mother of invention.Turning a threat into an opportunityIf I were to be an optimist, I could say that the current crisis is actually an opportunity. In fact, a major opportunity. My reading of the Iran War is that it is President Trump's strategic tit-for-tat against China for denying him rare earths and cutting off soybean purchases. In return Trump decided to deny China access to oil by closing access to Venezuela and Iran. Whether this will work, or whether the G2 condominium (read ‘surrender') will prevail, is unclear.But that is, in a sense, background noise that needs to be managed. India needs to focus on its own issues, of which I see several as critical, and the solution in general is to become Atmanirbhar, self-reliant, and from that, to create an Anti-Fragile nation:* National security/defense* Food security* Energy security* Digital security/narrative control* Trade securityThe first three do not need an explanation: they are obvious. Internal and external security are pre-requisites for any successful society. If India's hard-won food security can be threatened by external threats, then there needs to be some deep introspection. Energy security means diversification, both of hydrocarbon sources, and of types of energy, including renewables, nuclear, biomass, coal-based, and so on.Malign narratives and digital sovereigntyNarrative control is something that the Indian State has failed at so far; it is laughably easy to create hate speech against Indians and India (as has been demonstrated freely by any number of players, starting from the MAGA crowd, to Audrey Truschke to a”Cockroach Janata Party” and some nitwit Norwegian journalist in just the last fortnight) and there are no consequences to the culprits. It's enough to make me pine for Lee Kuan Yew's aggressive legal battles against the media.It's one thing if it were only a problem with foreigners, but with the massive spread of social media, and in particular generativeAI, it is becoming a serious domestic issue. Since India is an avid consumer of social media, and because generativeAI is trained on things like Wikipedia, X, Whatsapp and Google content, biased and motivated material becomes ensconced as The Truth. I have written about narrative warfare and manufacturing consent.This used to be a one-way tsunami of (mis)-information by legacy media, but now there is also the opposite: the wholesale and free vacuuming-up of Indian data (whatever happened to “data is the new oil”?). The “Great Firewall of China” both kept out foreign BIg Tech applications and prevented their plundering Chinese data: is that the way to go?Manufactured narratives are intended for regime change: all the color revolutions today are hatched with massive bot-farms funded by some combination of Deep State, CCP, ISI, Qatar etc. (for example the alleged Gen-Z uprisings that rocked Nepal, drove Sheikh Hasina out of Bangladesh). Thus muzzling malign narratives, and ensuring data security, are imperative.Even Singapore is not immune: it had to block anti-India narratives that likely originated from Chinese sources.A particularly striking example of narrative warfare is the virtual hate speech inducted into Wikipedia by deeply prejudiced anonymous editors. Ashley Rindsberg, who exposed the mighty New York Times' biases in his book The Gray Lady Winked, provides many examples of this.Of note to Indians and Hindus is his recent substack titled “Wikipedia's India War” where he identifies just four editors as having created most of the content condemning the Hindu American Foundation (HAF) in ‘Wikivoice', i.e. the allegedly neutral perspective of Wikipedia. They are, on the contrary, shown to be highly one-sided.As Rindsberg mentions, Wikipedia being central to generativeAI, the damage is baked into the world-view of all AI applications. Truly Orwellian. Says Rindsberg: “four… anonymous accounts can have an enormous impact on what millions of people believe to be the truth.” “Over four years (2021-2025), editors systematically erased HAF's identity as an American civil rights group, transforming its Wikipedia page into a heavily curated dossier of accusations.”Trade, and how the Spice Route was far superior to the Silk RoadFinally, something that is becoming increasingly important: ensuring freedom of trade. This is more than just freedom of navigation, although I find it instructive that Emperor Rajendra Chola sent a huge fleet 1,001 years ago simply to open up the Straits of Malacca. India can make an active attempt to regain primacy in Indian Ocean trade, the whole Pax indica idea.Here is another example of the power of narrative: we have been led to believe that the Silk Road to China was some major highway of commerce between ancient Rome and ancient China, but it was a term coined only in 1877 by the German Ferdinand von Richthofen. There was no highway. A large caravan might take six months, and with 500 camels traversing treacherous deserts and braving bandits, it might carry a maximum of 100 tons. That is puny.In comparison, on the Spice Route, a single stitched ship from Muziris could carry 400 tons of ivory, pepper, silk, tigers and elephants; and the historian Strabo around 1 CE talks about fleets of 250 ships going from Alexandria to India on a six-week monsoon-powered journey. That is 100,000 tons of merchandise. No wonder Pliny the Elder complained that Rome's treasuries were being emptied of gold by India.Simple question: where are hoards of ancient Roman coins found in Asia? Answer: not along the Silk Road. The hoards are in Kerala, Tamil Nadu and Sri Lanka.Today, it is possible for India to aspire to port-led development of trade, especially with the major ports at Trivandrum (Vizhinjam), Maharashtra (Vadhavan), and Great Nicobar (Galathea Bay). The underlying ‘software' of India's millennia-old trade competency was a ‘multi-protocol switch' as I pointed out, and today's India Stack can replicate that. Then there is the need for a blue-water navy: muscle to provide security on the Hormuz to Malacca sea-lanes.So there is a vision. How can India get there? This is where policy matters, as I discussed with policy expert Anuj Gupta. Policy, especially industrial policy, has had a bad reputation in certain circles because it was deemed to violate the virginal purity of classical capitalism. However, in a recent U-turn, even the World Bank admitted that industrial policy may not be all that bad, after all: the success of Japan, the Asian Tigers, and China can't be ignored.That leads to the question of why policy in India has produced mediocre outcomes, what is different now, and where the best use of policy might be.Industrial Policy: What went wrong in the past?There are many problems here. To begin with, the Soviet model, which Nehruvians swore by, was, in hindsight, a dead end. Second, there is the problem of governance: post-Independence bureaucrats have awkwardly borne the legacy of imperial hauteur and the needs of a developing society. Third, until recently, the bare necessities (food, electricity, road access) were not available to many citizens, and GDP growth was not their priority.There is also the culture of jugaad: of clever ways in which you overcome constraints through frugal improvisation and seat-of-the-pants making-do. This is fine for one-off things (e.g. converting a tractor trailer into a makeshift transport vehicle because your truck broke down), but it does not make for efficient and replicable industrial products. As The Economic Times said recently, it is time to junk jugaad. Quality has to become ingrained in people's minds.The issue of governance is significant: the bureaucracy and the judiciary have both under-performed, politicians, as everywhere, have been venal. It is said that China's growth can be attributed to the fact that its babus are engineers, and therefore with engineering ruthlessness move in straight lines. The US' babus are lawyers, and India's are humanities graduates. Well, engineers are not very good at second-order effects (eg. China's lurch from one-child policy to demographic collapse), but a little bit of ruthlessness is probably good.What is going reasonably well?There are a few modest success stories: for example, in electronics manufacturing or assembly. The PLIs (and DLIs) have produced the desired effort, with clusters of excellence where global suppliers have also set up shop (as they did earlier for the automobile industry in, say, Sriperumpudur). The fact that a lot of iPhones in the US are now imported from India is laudable, even though it may be derided as “screwdriver jobs”. That's where one starts the move up the value chain.The current semiconductor policy is a big hope, especially after the landmark agreement by the Dutch firm ASML with Tata Electronics in Dholera, Gujarat. Given that ASML has a near-monopoly position in Deep Ultraviolet Lithography (DUV) this is a major boost to India's chip ambitions. My recent conversation with AMD CTO Suraj Rengarajan went into India's chances to realize its ambitions.A recent announcement from Trivandrum-based fabless startup NetraSemi (a recipient of DLI) of the commercial availability of its edge AI chips is a landmark.Next is the newly announced plan for energy security revolving around both coal gasification and intensive offshore exploration. These fall squarely into the Atmanirbhar category: India simply cannot afford to have its energy held hostage by distant nations. It also needs distinctly Indian innovation.The Samudra Manthan initiative is also showing some promise. At least one out of three deep-water wells in the Andaman Sea (SriVijaya Puram-3) are reported to be showing the availability of natural gas, although it will take 5-10 years for this to be commercially available.What should the future look like for India's Industrial Policies?This of course is the hard question. Here is my personal perspective, and I accept that reasonable people may disagree. I think three areas need to be focused on, and will pay large dividends.* Drones and swarming software* Social media and AI stack* Maritime Trade and Blue-Water NavyI admit that these are not the only worthwhile industrial policies. Another is for copper, which would reverse the catastrophic effects of the closure of the Sterlite plant in Thoothukkudi, as the metal is an increasingly important component in electronics, data centers, etc., and far from being self-sufficient earlier, India now imports 50% of its needs. Another area of interest in quantum computing.There are also failures from which the right lessons need to be learned. The policy for EV batteries has apparently failed: according to Swarajya magazine, India has not been able to escape from near-total dependence on imported Chinese batteries.Drone swarmsI wrote recently that drones may well herald a step-change in warfare. For the moment, though, they are searching for their niche in offensive/defensive warfare. Drone hardware is already a well-trodden path with Chinese and other nations dominating it, although with IdeaForge, Paras, Garuda, IoTechworld Avigation etc., India is also making progress there. And India is indeed buying the hardware, $2 billion-worth, according to the Economic Times.But I believe the real game is in drone swarms. AI-based control software (similar to HiveMind) that would allow an entire swarm to act autonomously, just like a murmuration of starlings, would be the gold standard to aim for. Such a self-managing swarm would be virtually impossible to defend against, and I think India should put in place a PLI to support it, leveraging software capability in the country.Of course, drones are not just for military purposes, but also for commercial uses including things like logistics and agricultural use, such as precision delivery of fertilizer and pesticide to crops (as Garuda demonstrates). An Indian initiative that supports both drone hardware, and especially drone software, would be a potential winner.Digital Sovereignty: Social media and AI stackThere is a raging battle over which part of the AI stack India needs to invest in. As an old Unix hand, I believe the foundational model is not where the differentiation is. In analogy with Linux (the open-source Unix variant that was popularized by Linus Torvalds and an army of volunteers), there is little value in re-writing the operating system, but one can differentiate by building on top of it, or by judiciously choosing certain modules of it.Besides, the cost of building an entirely new foundational model would be astronomical and would consume the entire budget of IndiaAI Mission.Thus, my personal opinion is that the foundational model (especially when, it is believed, there are more or less open-source models available for free, e.g. Llama, DeepSeek) is not where India should expend its precious R&D resources, but on the layers of the stack above it. It is the data that matters, as Larry Ellison apparently suggests too.But there is the interesting counter-example of Sarvam AI which is producing its own sovereign model: multi-lingual and presumably otherwise tuned to Indian needs. The question is whether this can survive when hundreds of billions worth of capital investment are going to the US Big Tech companies and their Chinese rivals. The sad history of Koo, a Twitter rival, comes to mind. So does Arattai, a Whatsapp rival, whose popularity has waned. .A well-thought-through industrial policy on generativeAI is therefore essential. The status quo ante is unsustainable; given the fact that Sarvam has also found it difficult to raise funds in the US, it is worth pondering whether a China-style massive subsidy is the answer. And where should it go, into foundational models or into the layers of the stack above it? The answer is “both”, but with priority to the latter.Here is where I would prioritize investments, in order:* Vertical applications in specific domains: e.g. defense, healthcare, agriculture, governance (particularly in the judiciary and in ease of doing business in the bureaucracy)* Fine-tuning and customization: for the needs of the Indian context, e.g. multi-linguality under Bhashini* Compute infrastructure: GPUs, sovereign and protected indian datasets* Sovereign Small-Language Models such as Sarvam AIAs mentioned above, at the moment India's data is being sucked up for free by US Big Tech. In addition, there is the real danger that Indic Knowledge Systems will be mined and digested, as has happened to yoga, pranayama, etc., which have been given Western analogs and nomenclature, as in Pilates, ‘coherent breathing' etc.These two problems are connected, and both need to be tackled in parallel. Social media is being weaponized against India, and this is magnified by the legacy media in a positive feedback loop. Three examples: one was the rage against Adani based on the dubious research of Hindenburg, which then went under; the second is Bloomberg's reckless accusation about gold reserves being sold by the RBI, which they were forced to retract, but social media and Wikipedia will remember it; the third is the meteoric (media) rise of the Cockroach Janata Party.Trade using major ports, Digital Public Infrastructure and a blue water navyUsing trade for competitive advantage is an age-old tactic. The trade tiffs between the US and China are examples of this: we are witnessing war by other means. Many nations are getting into this act, and India does have some advantages, partly based on geography. Maritime trade is likely to continue to be the key, which makes naval chokepoints the big story, but not the only story to watch out for.The major aspects of maritime trade include infrastructure, the digital “multi-protocol switch”, and security. On the one hand, India is developing not only major container ports, and the road/rail links to get to them, and the industrial goods to ship out through them, but also a serious shipbuilding industry, which was one of India's historical strengths. Then it used to be stitched wooden ships (teak beams lashed together with coconut rope). Now it's modern steel ships.There are the big, efficient new ports, which can now turn ships around with Singapore-like efficiency; the proposed third aircraft carrier group which will make it possible to patrol the Arabian Sea and the Bay of Bengal at the time; the Air-Independent Propulsion diesel submarines and nuclear submarines that can monitor (and if necessary, deny) narrow straits; the sale of supersonic Brahmos cruise missiles to the Philippines, Vietnam and Indonesia (and Cyprus) that create ship-denial zones: all this is muscle.And the final piece, the ‘software' for trade, the “multi-protocol switch”. This last is complicated. Its value is underestimated by many. But this is what enables friction-less transactions between various unrelated parties. The India Stack and the Digital Public Infrastructure can be utilized to provide such a facility. But it is complex enough to need significant study as to what is possible, and how to roll it out.Second-order effectsIn closing, it is worth considering some of what the (unintended) consequences of these proposals may be. Let us note that the G2 has no interest in allowing India to grow and make it a G3. They will do everything in their power to kneecap India, by all means possible.There is also a certain derision for India in some circles. Here is a generic western opinion on why China got rich, and India didn't. Well, the author doesn't consider the second-order effects of the wholesale destruction of Chinese civilization: that is a tradeoff Indians may not prefer for themselves. We all know how China's well-intentioned One Child Policy turned into demographic collapse within a few years. Besides, as The Economist asks, “China is innovative. Its economy is a mess. Which will win out?”This is why I think planning for these second-order effects is important. We tend to ignore them because they seem counterintuitive or unlikely, but Nassim Taleb has sensitized us to how low-probability Black Swan events can have grave consequences.As an example, attempting digital sovereignty may have unwelcome side-effects: Big Tech have the first-mover advantage and network effects and there are increasing returns to scale. They will surely make it hard for a new player to break in. Besides, the large investments in data centers and GCCs that they are making in India would make it very difficult for them to be ejected with a “Great Indian Firewall”.Even taxing their capture of Indian data will be complicated; not to mention that they have demonstrated that they can happily violate copyright laws with no consequence; therefore they will find ways to chew up and spit out Indian Knowledge Systems, and essentially re-colonize India. Digital colonialism is not a threat, it is a reality today, and it is a consequence of the relatively open Indian system.In addition, there is a malign group, the “barbarians within” as Arnold Toynbee once put it, who are ready to sacrifice Indian sovereignty for a pittance.Given all this, it will be very difficult to put in place serious measures to gain digital independence; and the narrative-peddling is likely to gain further momentum: just consider the caste allegations that have haunted BAPS in the US (despite the cases being dismissed by the US DoJ), the Cisco Systems case where, again, the case was dismissed, but the narrative continues, and the persistent efforts in various US states to turn caste into a weapon to bludgeon Indians.Another sensitive issue is that of the multi-protocol switch for trade. While from an Indian point of view, it eases trade and harks back to a Golden Age of Indic maritime commerce, but that will be viewed elsewhere very differently, for instance by the US as an attempt to de-dollarize. The US has jealousy guarded – with very good reasons that we will not go into here – the dollar's reserve currency status.We have also seen what happened to those who attempt to hurt the dollar's primacy: in 1985, the Plaza Accord devalued the dollar, and that was a body blow to Japan's economy, which has not recovered its mojo to this day. Later, Iraq's Saddam Hussein and Libya's Muammar Gaddafi both had ideas about replacing the petro-dollar with, respectively, the Euro and a new pan-African gold-backed currency. We know what happened to them.If the India Stack multi-protocol switch is perceived as an alternative to the US dollar, there may be grave consequences. Therefore, it should be conceived and deployed only as an adjunct to it and to the almighty SWIFT settlement system.ConclusionIndia is at a crossroads now. Even though the Hormuz closure is a serious problem, if it plays its cards right, adversity can be turned into opportunity across a variety of perspectives. The key is Atmanirbhar, self-reliance. If India can now implement a crash program of industrial policy, and at the same time overcome an ingrained Third-World tendency to cut corners, it can finally break free of the years of underperformance, what I called the Nehruvian Penalty in 2004.It is possible, but there are caveats: unforeseen consequences. Hic sunt dracones. Here be dragons. Be afraid. Be very afraid.3700 words, 7 June 2026This is episode 192 of the Shadow Warrior podcast. Here is a companion AI-generated slideshow. (Note that the borders of India are not necessarily depicted correctly here, because it is generated by an AI, notebookLM.google.com) This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit rajeevsrinivasan.substack.com/subscribe

An airhacks.fm conversation with Alvaro Hernandez (@ahachete) about: discussion about the quarkus Insights episode "#337 The Database Cloud" stackgres live demo, StackGres as a Quarkus and GraalVM native kubernetes operator for running Postgres, comparing CloudNativePG (CNPG) by EnterpriseDB to StackGres, Patroni for Postgres high availability, the split-brain risk of relying on Kubernetes and etcd alone, distributed consensus and leader lock election via etcd, why distributed systems and cryptography should not be self-implemented, async, synchronous and quorum (semi-synchronous) Postgres replication trade-offs, cascading and cross-region replication topologies, the false-positive problem and heuristic exceptions in two-phase commit, the ondb ("own your database") project for self-hosted Postgres, losing control with managed cloud services and untestable backups, vanilla unmodified Postgres on StackGres, the "Kubernetes without Kubernetes" (Kubeless) pattern, talking directly to ContainerD through the CRI API, runc and the Docker to ContainerD chain, a self-contained native binary that embeds ContainerD over Unix domain sockets, the slony node-local component named after the Postgres slonik elephant mascot, the Matriarch orchestrator component, reverse gRPC tunnels with Slonies phoning home across NAT and firewalls, a multi-tenant cloud control plane provided as a service, curl-pipe-shell node installation with a token, end-to-end encrypted Postgres protocol tunneling for JDBC from anywhere, psql compiled to wasm in the web console, Tailscale-inspired user experience, unifying nodes, Kubernetes clusters and cloud pools as resources, Slony Kubernetes controller, Java 25 source-mode scripting without dependencies, implementing your own MCP server for Postgres JDBC metadata, the Goose agentic UI donated by Block to the Linux Foundation, AI Rails BCE, Java, Web Components skills Alvaro Hernandez on twitter: @ahachete

.NET on FreeBSD 15, Klara and TrueNAS fixing dedup, dhcpcd and unbound in FreeBSD Jails, and more... NOTES This episode of BSDNow is brought to you by Tarsnap and the BSDNow Patreon Headlines Running .NET 10.0 on FreeBSD 15.0 How Klara and TrueNAS collaborated to fix one of ZFS's longest standing limitations News Roundup Back to FreeBSD: Part 1 dhcpd and unbound in FreeBSD jails How our environment still needs the security boundary of Unix logins Increasing a bhyve vm disk Tarsnap This weeks episode of BSDNow was sponsored by our friends at Tarsnap, the only secure online backup you can trust your data to. Even paranoids need backups. Feedback/Questions Send questions, comments, show ideas/topics, or stories you want mentioned on the show to feedback@bsdnow.tv Join us and other BSD Fans in our BSD Now Telegram channel

This show has been flagged as Clean by the host. This series is dedicated to exploring little-known—and occasionally useful—trinkets lurking in the dusty corners of UNIX-like operating systems. Most users of UNIX-like systems are probably familiar with the diff utility. It is widely used with source code to compare two files and see what the differences are between them. Non-programmers, like me, also use it to examine what has changed in different versions of scripts or configuration files. Quite a few pieces of newer software can compare different versions of data and express changes in a format either identical to or similar to diff output. However, there are two other long-standing tools for this purpose that are far less known and deserve in my view to be termed UNIX Curios. The first of these is cmp 1 . While diff is primarily intended to be used on text files and compares them line by line, cmp compares files byte by byte. In my experience, its main use is to see whether two binary files are in fact identical—if they are, cmp outputs nothing and returns an exit status of 0. Back when methods of transferring files were not as reliable as they are today, this was a tool I would reach for sometimes. For example, you could use it to confirm that the data on a CD-ROM you burned was the same as the original. If there is a difference between the files, cmp will return an exit status of 1. By default, it will also print the location (byte and line number) of the first differing byte. When used with the -l option, it will print the location and value of every byte that differs. There is one exception to these: if the files are the same except that one is shorter than the other, it will print a message to that effect. The exit status will still be 1 in that case. Using the -s option with cmp will cause it to be totally silent and output nothing. Only the exit status will indicate whether the files are the same, different, or if the exit status is greater than 1, that an error occurred. This makes it useful for scripting, for example in case you wanted to confirm that a file copied to another location arrived fully intact. It is worth noting that diff is also capable of comparing binary files—however, it is not required by POSIX to report what is actually different or where differences occur. The same exit status as in cmp is returned: 0 if the files are the same, 1 if they are different, or greater than 1 if an error occurred. While many implementations offer an option to suppress the output, this is not in the standard 2 so the most portable method would be to instead redirect output to /dev/null . On my system the diff utility is three times the size of cmp , so if you don't need its extra capabilities, it is a less efficient way of doing the job. The other UNIX Curio for today is comm , and this utility 3 is also intended to compare two files to see what is common between them. Ken Fallon briefly talked about it a few years ago in HPR episode 3889 . Compared to the others, it has a much more specific use case. The two files are expected to be text files that are already sorted. What comm will do is print a tab-separated list of all the lines appearing in either or both files. Lines only in the first file will appear in the first column, lines only in the second file will be in the second column, and lines in both files will be in the third column. Any combination of the options -1 , -2 , and -3 can be used with comm to suppress printing of the first, second, or third column respectively. Using all three options at the same time is supported but it results in no output, so that isn't very useful. Unlike the other utilities, the exit status of comm doesn't tell you anything about the two files. It will be 0 if the program ran successfully, and greater than 0 if it didn't. I'm not sure if I have ever actually used comm for anything practical. I find its default output a bit difficult to meaningfully interpret, plus you need to ensure the two files are already sorted. It seems to be best suited to comparing lists, and one use case that Ken Fallon mentioned would be comparing two lists of files to see if any are missing. The command comm -3 listA listB would print files that only appear in listA in the first column and those only in listB in the second column. This would let you ignore all the filenames that appear in both and focus on those that were absent from one or the other. If on the other hand you only wanted to see the filenames that are on both lists, comm -12 listA listB would give you that. Some more frivolous potential uses also come to mind. If for some reason the cat utility is broken on your system, you could use comm listA /dev/null to print the file listA instead. If you want to insert tab characters before every line of a file but have an aversion to using sed or awk , then comm /dev/null listA would output listA with one tab before each line, and comm listA listA would insert two tabs. A bit silly, but it would work. The GNU implementation of comm even lets you choose something other than a tab to separate the columns 4 , so you could go wild with that. According to the POSIX specifications for cmp and comm , one of the two filenames given as arguments, but not both, can be a " - ", in which case standard input will be used for that "file" in the comparison. Also, the results are undefined if both arguments are the same FIFO special, character special, or block special file. Some implementations might not have these limitations, but you shouldn't rely on that everywhere. All three of these were developed quite early. The cmp utility appeared in 1971's First Edition UNIX 5 , while comm and diff seem to have made their debut in Fourth Edition UNIX 6,7 from 1973. The original versions might not have behaved exactly like their modern counterparts, and newer implementations (especially of the diff utility) have acquired additional options and capabilities, but the basic operation of each has stayed the same. The next time you need to compare files against each other, consider whether cmp or comm might be appropriate before automatically reaching for diff . They all have their uses in different situations. References: Cmp specification https://pubs.opengroup.org/onlinepubs/009695399/utilities/cmp.html Diff specification https://pubs.opengroup.org/onlinepubs/009695399/utilities/diff.html Comm specification https://pubs.opengroup.org/onlinepubs/009695399/utilities/comm.html GNU coreutils manual: comm https://www.gnu.org/software/coreutils/manual/html_node/comm-invocation.html First Edition UNIX cmp manual page http://man.cat-v.org/unix-1st/1/cmp Fourth Edition UNIX comm manual page https://www.tuhs.org/cgi-bin/utree.pl?file=V4/usr/man/man1/comm.1 Fourth Edition UNIX diff source https://www.tuhs.org/cgi-bin/utree.pl?file=V4/usr/source/s1/diff1.c Provide feedback on this episode.

Microsoft Build 2026 announced an end-to-end agentic AI stack. COMPUTEX Taipei confirmed heterogeneous AI infrastructure across ARM, Marvell, Intel, Qualcomm, and NVIDIA. Alphabet raised $80 billion. Cisco Live repositioned the network as the AI platform. Patrick Moorhead and Daniel Newman break it all down alongside earnings from Broadcom, HPE, Palo Alto Networks, and CrowdStrike, plus the token cost conversation, the edge AI push, and what Palantir and Oracle are saying about proprietary data as the real AI moat. The handpicked topics for this week are: Microsoft Build 2026 Announced an End-to-End Agentic AI Stack: Microsoft shipped MAI-Thinking-1, its first homegrown thinking model, alongside Scout, Microsoft IQ, Project Solara, and a Majorana 2 quantum update targeting a 2029 commercial timeline with claims of a 1,000x reliability gain. Pat describes MAI-Thinking-1 as likely better than Sonnet 4.6 in blind testing and delivering close to GPT 5.5 quality at a far lower cost. Scout is Microsoft's first autopilot agent, anchoring the M365 Agent Suite with Office Pilot Agent Mode and Agent 365. Microsoft IQ serves as the context layer, integrating M365, business data, boundary IQ, and web IQ with GitHub Copilot, Foundry, and Copilot Studio. Project Solara is a new Android-based platform built for agent-first devices across transportation, retail, and hospital settings. Microsoft also added 83 Unix commands to the Windows stack. Dan frames Microsoft's real play as distribution, not frontier model development, noting that the open model ecosystem being pulled into the platform will matter more to CFOs managing token costs at scale. (The Decode) The AI Stack Goes Multi-Silicon — COMPUTEX Taipei 2026 Confirms Heterogeneous AI Infrastructure: ARM's AGI CPU is in production with Google moving its TPU head node to ARM, and adding Oracle and ByteDance as new customers. ARM also introduced a new switch, the TT100, and put the 51T CPO switch on stage. Marvell received a trillion-dollar company endorsement from Jensen Huang, adding $90 billion in market cap on the comment alone. Intel announced disaggregated inference details and Xeon 6+ Clearwater Forest, its first 18A data center processor. Vista Equity and Cambium Capital announced a NeoCloud called Vector Core Compute, with Xeon 6 handling orchestration, Salmonova RUs handling decode, and Blackwell GPUs handling pre-fill. Qualcomm's Cristiano Amon announced the Dragonfly data center brand with Snapdragon C details coming at their June investor day. The WSTS raised the 2026 semiconductor TAM forecast by 90% to $1.51 trillion, with Pat noting the market could hit a trillion dollars if memory is excluded entirely. (The Decode) NVIDIA RTX Spark and the Edge AI Push: NVIDIA coordinated with ARM and Microsoft around the RTX Spark at COMPUTEX, with the shared message being that the future of Windows is here. Signal65's Ryan Shrout asked Jensen directly why NVIDIA wants to be in the PC business, given low margins and diminishing returns. Dan frames the answer in the context of devices increasingly becoming mobile data centers, capable of running models at much greater efficiency than cloud delivery. The edge AI conversation is also directly tied to token cost economics: as intelligence delivery moves closer to the device, the cost per token drops significantly. The jury is still out on whether NVIDIA will meaningfully disrupt the PC market, but its influence over OEMs like Lenovo and Dell that depend on it for data center gives it real leverage over SKUs. (The Decode) Token Economics and Frontier Model Cost Pressure: Dan and Pat discuss a substantive shift in how enterprises are thinking about AI consumption costs. Dan argues that "token maxing," the practice of defaulting to the most powerful frontier model for every task, has now effectively peaked, as bills have come due at scale. Companies paying for tokens in volume are starting to question whether they can afford the prices that frontier models actually cost to deliver. Pat pushes back, saying the dynamic is still present, but both analysts agree that the market is moving toward a model where token selection is matched to the job, with Microsoft's MOE approach and thinking models positioned to help CFOs manage that economics story. (The Decode) Continuum Goes Public at Highest Valuation for an AI Platform: Dan notes that Continuum, the Honeywell-spawned quantum company, went public this week at what he calls the highest valuation for an AI platform to date. He flags that IonQ will likely contest that characterization. The broader context is Microsoft entering the quantum conversation with Majorana 2 at Build, a name that has largely been absent from the quantum race, while IBM has received most of the attention. (The Decode) AI CapEx Has Outgrown Cash Flow — Alphabet's $80 Billion Equity Raise: On June 1, Alphabet announced an $80 billion equity capital raise, upsized to $85 billion, structured as $40 billion ATM, $30 billion underwritten, and a $10 billion private placement with Berkshire Hathaway anchoring. Pat frames the questions over CapEx returns as entirely dependent on whether you are an AI boomer or a doomer: if the payback comes, the raise is the right move. If it does not, the math doesn't close. Dan argues the investment is existential, drawing parallels to how infrastructure-first companies have always spent ahead of monetization, and notes that Google's equity is being used as a capital engine that may be more efficient than the debt markets right now. Both analysts flag the downstream implications for Broadcom, MediaTek, and Marvell given the TPU connection. (The Decode) The Network Becomes the AI Platform: Cisco Live 2026: Cisco launched Silicon One P200, the Secure AI Factory with NVIDIA and Spectrum X, AgenticOps, MCP-native automation, Cisco IQ, LiveProtect, and folded Astrix Security and Galileo into Splunk under one control plane. Pat identifies Cisco Cloud Control as the biggest announcement of the entire show, pulling together Catalyst, Meraki, Nexus, Firewall, and WebEx under agentic ops that run natively through MCP, with code running directly on smart switches that have x86 processors. Pat also credits Cisco for establishing Silicon One as a credible chip alternative for hyperscalers capable of taking on Tomahawk and Jericho. Dan frames the long-term opportunity as campus and branch enablement when industrial AI and robotics deployments accelerate, arguing that the numerator of AI's economic impact has barely started, as edge deployment spending has not yet begun. (The Decode) The Flip: Did Microsoft Build 2026 Effectively End the OpenAI Partnership? Pat argues the divorce decree has been filed. MAI-Thinking-1 was built with zero distillation from third-party models offering clean enterprise data lineage, with Maia 200 in production plus Anthropic chip supply, which signals vendor hedging. OpenAI is going all-in on AWS, which means you cannot be married to two people, and the full Build stack covering model, OS containment via MXC, agents via Scout and Agent 365, and context via Microsoft IQ removes every architectural dependency on OpenAI. Dan counters that Microsoft is hedging rather than leaving and predicts the partnership will run through the decade. Enterprise Copilot customers are explicitly showing in data that they demand GPT 5.5, internal benchmarks have not been independently validated, and Microsoft stands to make meaningful money from the OpenAI IPO. (The Flip) Broadcom Q2 FY26 Earnings: Broadcom posted revenue of $22.19 billion, a narrow miss depending on which consensus data set is used, with EPS of $2.44 beating estimates and AI semis at $10.8 billion. Hock Tan declined to raise the $100 billion full-year AI chip target, and the stock dropped 13% in premarket trading. Q3 guide came in at $29.4 billion. Pat calls the miss a timing issue driven by Google's multi-sourcing across Marvell, MediaTek, and Broadcom rather than a fundamental problem. Dan flags that Hock Tan opened the earnings call by accidentally reading from the 2025 print, calling it "not the best moment." Sell-side re-ratings held in the 500s across Jefferies, Mizuho, and Deutsche Bank despite the drop, with Futurum Equities having it at 600. (Bulls and Bears) Hewlett Packard Enterprise Q2 FY26 Earnings: HPE delivered revenue of $10.68 billion, up 40% year over year, and EPS of $0.79, up 100%. Juniper integration and AI servers both outperformed, and all FY26 guides were raised. The stock jumped 19% after hours before settling into a roughly 15% gain, with HPE up 68% over the last month. Pat frames HPE as a value play rather than a volume play, methodically targeting enterprise and sovereign cloud deals where it can maintain profitability, rather than competing for massive NeoCloud volume. Antonio Neri was clear on the call that the profitability pull-forward is a one-shot deal. Pat and Dan will both be at HPE Discover the week after next to interview Neri and the C-suite. (Bulls and Bears) Palo Alto Networks Q3 FY26 Earnings: Palo Alto posted revenue of $3.0 billion, up 31% year over year, beating the $2.94 billion estimate, with non-GAAP EPS of $0.85, beating the $0.79 to $0.81 range. NGS ARR reached $8.1 billion, up 60% year over year, including $1.6 billion from CyberArk and Chronosphere. RPO hit $18.4 billion, up 36%. Both FY26 revenue and EPS guides were raised. Adjusted FCF margin came in at 38.5% TTM, up 430 basis points. The stock jumped 11% immediately after hours, then drifted lower. Pat points to 2,200 platformized customers and 120% net retention as the most important metrics. Dan notes the SaaSpocalypse thesis continues to be wrong. (Bulls and Bears) CrowdStrike Q1 FY27 Earnings and the Proprietary Data Moat Argument: CrowdStrike posted revenue of $1.39 billion with EPS of $1.10 and ARR of $5.51 billion. Net new ARR of $255.8 million set a Q1 record, up 32% year over year. FY27 net new ARR guide was raised by $52 million to a $1.29 billion midpoint, and FY27 revenue was raised to $5.915 to $5.959 billion. A 4-for-1 stock split was announced effective July 2nd. The stock dropped 11% despite the beat after a 64% year-to-date run into earnings. Dan uses the results to make a broader argument against the software disruption thesis, referencing Palantir CEO Alex Karp daring customers to build without him using Anthropic or OpenAI, and Larry Ellison's argument that the real AI value unlock sits in proprietary enterprise data that is not accessible to frontier models. Enterprises with governed, secure, proprietary data will continue to need platforms like CrowdStrike regardless of what frontier models can do. (Bulls and Bears) Six Five Summit is coming. Salesforce CEO Mark Benioff will kick off the event. Register and stay current at sixfivemedia.com/summit. Watch the full video at sixfivemedia.com, and be sure to subscribe to our YouTube channel so you never miss an episode.   The Decode Microsoft Declares Independence — Build 2026 Ships an End-to-End Agentic AI Stack (MAI-Thinking-1 + Scout + Microsoft IQ + Project Solara + Majorana 2) https://www.theverge.com/tech/941738/microsoft-build-2026-biggest-announcements The AI Stack Goes Multi-Silicon — Computex 2026 Confirms a Heterogeneous AI Infrastructure (ARM + Marvell + Intel ASIC + Qualcomm + RTX Spark); WSTS Raises 2026 Semi TAM Forecast 90% to $1.51T https://www.tomshardware.com/tag/computex AI Capex Has Outgrown Cash Flow — Alphabet's $80B Equity Raise Is the Largest in U.S. Corporate History; Berkshire Anchors $10B https://abc.xyz/investor/news/news-details/2026/Alphabet-Announces-Proposed-80-Billion-Equity-Capital-Raise-to-Expand-AI-Infrastructure-and-Compute-2026-b0myAMewCa/default.aspx The Network Becomes the AI Platform — Cisco Live 2026 Launches Silicon One P200, Secure AI Factory (with NVIDIA), AgenticOps, Astrix Security + Galileo https://www.cisco.com/site/us/en/about/whats-new/index.html The Flip Did Microsoft Build 2026 Effectively End the OpenAI Partnership? MAI-Thinking-1 Beats Sonnet 4.6 in Blind Testing, Microsoft Claims GPT-5.5 Parity at 10x Cost Efficiency — Will MS Quietly Wind Down OpenAI Exclusivity by FY28, or Is OpenAI Still the Frontier Anchor Microsoft Needs?   FOR:  MAI-Thinking-1 beating Sonnet 4.6 in blind preference + GPT-5.5 parity at 10x cost efficiency is a frontier-model independence proof point https://www.latent.space/p/ainews-microsoft-build-mai-thinking Build 2026: Accumulating Evidence of Microsoft's AI Independence — EDN (June 4) — https://www.edn.com/build-2026-accumulating-evidence-of-microsofts-ai-independence/ Maia 200 in production + Anthropic-Maia chip talks signal Microsoft is hedging its inference vendor stack https://blogs.microsoft.com/blog/2026/01/26/maia-200-the-ai-accelerator-built-for-inference/ Microsoft canceled Anthropic's internal software licenses + pivoted to chip-supply pursuit — customer-not-competitor positioning https://www.cnbc.com/2026/05/21/anthropic-microsoft-maia-200-ai-chip.html   AGAINST:  Enterprise Copilot customers explicitly demand GPT-5.5 — internal benchmarks don't replace the brand https://learn.microsoft.com/en-us/microsoft-365/copilot/release-notes?tabs=all MAI-Thinking-1 benchmarks haven't been third-party verified — Microsoft is the only source https://www.latent.space/p/ainews-microsoft-build-mai-thinking The MS-OpenAI partnership is contractual through 2030+ — unwinding it is impractical and expensive https://blogs.microsoft.com/blog/2026/04/27/the-next-phase-of-the-microsoft-openai-partnership/ Microsoft's actual strategic risk is OpenAI leaving, not MS leaving — Anthropic + OpenAI IPOs make OpenAI exit risk the real concern https://www.anthropic.com/news/confidential-draft-s1-sec Bulls & Bears Broadcom (AVGO) Q2 FY26 ACTUALS — Rev $22.19B (Narrow Miss) + EPS $2.44 (Beat); AI Semis $10.8B; Hock Tan Refuses to Raise the $100B Full-Year AI Chip Target — Stock −13% Premarket; Q3 Guide $29.4B https://www.cnbc.com/2026/06/03/broadcom-avgo-earnings-report-q2-2026.html Hewlett Packard Enterprise (HPE) Q2 FY26 ACTUALS — Blowout: Rev $10.68B (+40%), EPS $0.79 (+100%); Juniper Integration + AI Servers Both Outperform; FY26 Guides All Raised; Stock +19% AH https://www.businesswire.com/news/home/20260601866494/en/HPE-Reports-Fiscal-2026-Second-Quarter-Results Palo Alto Networks (PANW) Q3 FY26 ACTUALS — Beat-and-Raise: Rev $3.0B (+31% YoY, Beat $2.94B), Non-GAAP EPS $0.85 (Beat $0.79-0.81); NGS ARR $8.1B (+60% YoY, $1.6B from CyberArk + Chronosphere); RPO $18.4B (+36%); FY26 Revenue + EPS Guides BOTH RAISED; Adj FCF Margin 38.5% TTM (+430 bps); Stock +11% Immediate AH, Then Drifted Lower https://www.paloaltonetworks.com/company/press/2026/palo-alto-networks-reports-fiscal-third-quarter-2026-financial-results CrowdStrike narrowly beats estimates on AI tailwinds, but stock falls 9% — CNBC (June 3) — https://www.cnbc.com/2026/06/03/crowdstrike-crwd-q1-2027-earnings.html  

OpenBSD 7.9, Critical Infrastructure in FreeBSD, GhostBSD Finance report, Solaris 11.4 updates, and more... NOTES This episode of BSDNow is brought to you by Tarsnap and the BSDNow Patreon Headlines OpenBSD 7.9 60th Edition has been released and Reported over on Undeadly Cleaning Up Critical Infrastructure in FreeBSD News Roundup Apple Wants to Kill Your Time Capsule but They Run NetBSD So They Can Not Oracle To Reduce The Frequency Of Solaris 11.4 Updates FreeBSD on a Thinkpad T14 Gen 2 Intel January 2026 Finance Report Beastie Bits The DragonFly site has a recently-updated page describing how DPorts is assembled and the process to contribute. TUHS - Unix use of VAX protection modes Origin of the rule that swap size should be 2x of the physical memory - The Duke and the Beastie - Improving OpenJDK support for FreeBSD Tarsnap This weeks episode of BSDNow was sponsored by our friends at Tarsnap, the only secure online backup you can trust your data to. Even paranoids need backups. Feedback/Questions Send questions, comments, show ideas/topics, or stories you want mentioned on the show to feedback@bsdnow.tv Join us and other BSD Fans in our BSD Now Telegram channel

This show has been flagged as Clean by the host. This series is dedicated to exploring little-known—and occasionally useful—trinkets lurking in the dusty corners of UNIX-like operating systems. In UNIX Curio #4 ( HPR episode 4617 ), I teased the subject of file compression. Today I'm circling back to that. The history of data compression goes back at least to the 1970s, and in contexts outside UNIX and computers, probably even earlier. Somehow, it is refreshing to learn that humans have always struggled to have enough storage space to keep all the data they want to hang on to. One way around this limitation is to use some form of compression. I am only going to dive into lossless compression for this episode—that is, a compression method that can be reversed and will spit out the original data bit for bit. Lossy compression methods also have their places: you might be familiar with their use for audio (such as Ogg Vorbis or MP3); it's also used for images (such as JPEG). Lossy compression allows some of the original data to be thrown away, resulting in a smaller file than is possible with lossless compression, but the intent is for the result to still sound or look "good enough" to a human observer. Also, I am going to limit my discussion to generic methods used for many types of data; while FLAC does lossless compression, it is specifically designed just for audio. I should make clear that I have never studied computer science or information theory, so this episode will not get into the science behind various types of compression algorithms and how they differ. But in general, these methods take advantage of the fact that many types of data have recurring patterns. English text mostly consists of words that often re-appear many times—source code similarly has keywords and variable names that recur. Compression is accomplished by representing a piece of data that occurs multiple times with a symbol that is shorter in length. The first compression program in the UNIX world I could find is called pack , from 1978 1 . It was shortly followed in 1979 by a similar program called compact 2 . Both of these used a technique called Huffman coding, but with some differences between them. Files compressed with pack were given a .z extension and compact gave filenames a .C extension. Roughly every five or ten years after this, a new program would come along and achieve lasting popularity. There were, and still are, two opposing forces facing any new form of compression. Working in favor was the advantages it provided—first among these was achieving a better compression ratio, but performance improvements such as speed or reduced memory usage could also be compelling. The force against any new method was the fact that it was not yet widely supported—it doesn't much help to have a smaller file if the people you share it with cannot decompress it. The next major advance in compression arose out of three scientific papers: two in 1977 and 1978 by Abraham Lempel and Jacob Ziv (called LZ77 and LZ78), and one by Terry Welch in 1984 which built on LZ78. This last method is typically referred to as LZW. Our UNIX Curio for today is a program called compress 3 that implements the LZW method. Files compressed this way are named with the extension .Z . I had always assumed that this was to honor Jacob Ziv, but now that I've researched the history, it seems more likely to be a follow-on from how files compressed by pack were named. Since pack did not use any of the Lempel-Ziv methods, I would guess that it used .z because that wasn't already taken by anything else, but that's pure speculation. I do recall encountering .Z files in the wild, but feel certain that hasn't happened in the last 25 years, maybe longer. If you need to expand one of these, uncompress 4 is the program to use ( GNU's gunzip can also handle them 5 ). However, there was a serious problem that arose with the LZ78 and LZW compression methods. Both of them were patented, and the owner became aggressive in seeking payment from developers and users. The compress utility was developed within two months of the publication of Welch's 1984 paper and was included in Bell Laboratories' Eighth Edition UNIX before these shakedowns started. The paper did not disclose that a patent had been filed, and apparently Spencer Thomas and the other developers of compress were unaware of it. The utility became popular for a while, and was even standardized by POSIX, but people moved away from LZW once the legal threats started. Another important advance came in 1991 and was called the DEFLATE compression method. It combined the un-patented LZ77 method with Huffman coding to achieve a similar level of compression as LZW (actually, often better) without the legal trouble. DEFLATE was developed for PKZIP and was soon adopted by the GNU project's gzip compressor. While Phil Katz (the "PK" in PKZIP ) patented one way of implementing the DEFLATE method, it was possible to write a compressor and decompressor without infringing 6 ; also, he apparently never tried to enforce the patent 7 . As I mentioned in UNIX Curio #4, .zip is both an archive and a compression format. Each archive member can be compressed with one of several possible methods (or stored without compression). Unlike a tar file where compression can be applied to the entire archive, in .zip each archive member is compressed individually. This often means a .zip file will be slightly bigger than a tar file with the same contents compressed with gzip , because the .zip format cannot take advantage of duplication that occurs among more than one member of the archive. The vast majority of .zip files use only the DEFLATE and uncompressed storage methods and these are the only options if you want to follow the profile standardized in ISO/IEC 21320-1. Actually, since they both use DEFLATE, gzip is able to extract a .zip file in the special case where it only holds one member compressed with that method. From the 1990s onward, people paid significant attention to avoiding patent landmines, so only methods that didn't have that problem became broadly popular. While the patents on LZ78 and LZW have since expired, I feel like their most successful legacy was in discouraging people from using those methods, leading to DEFLATE taking the popularity crown. The next step came in 1996 and 1997 with the development of bzip and bzip2 by Julian Seward. The original method was quickly followed by bzip2 , which was the version that achieved true popularity. They use the Burrows-Wheeler transform, which does not itself compress data but re-arranges it to make it more compressible; this is combined with other techniques 8 . (At least, that's my understanding. I told you, I'm not up on information theory.) This provides a significant reduction in the compressed size of the data compared to earlier methods—however, it is slower than DEFLATE both during compression and decompression. Separate projects have developed parallel versions of gzip and bzip2 that can take advantage of multi-processor machines, but the original utilities run single-threaded. Another five years later, in 2001, Igor Pavlov added the Lempel-Ziv-Markov chain algorithm (LZMA), an enhancement to LZ77, to his 7-Zip compression tool. This was followed a few years later by LZMA2, a container format that allowed for LZMA compression to be split between multiple threads. Broad LZMA2 support came to the UNIX world in 2009 with the xz utility 9 . It offers roughly similar compression ratios to bzip2 , though it can be better or worse depending on the data to be compressed. While compression generally takes even longer than bzip2 , decompression is significantly faster (though still not as fast as gzip ). The Linux kernel relatively quickly supported booting from xz-compressed images 10 because it was a good match for that use case—compression, the time-consuming activity, only has to be done once while the more frequent decompression during boot happens relatively fast. The last method I will cover is Zstandard 11 , often written as zstd . This came about in 2015, and is another variation on LZ77 that uses finite-state entropy (which means nothing to me, but you might understand it). It performs about as well as DEFLATE in terms of compression ratios, but is much faster both when compressing and decompressing data. I should say that these statements are true with the typical default settings—depending on the compression level selected, it can compress more slowly, but compress the data smaller. However, decompression is always speedier than DEFLATE. This makes it attractive for some uses, and it is heavily promoted by Meta/Facebook, where Yann Collet developed it. For example, shipping large amounts of actively-used data between machines in a data center can go more quickly when the size is reduced; however, if the compression and decompression steps take too long that benefit is lost. A speedy method can be valuable even if it doesn't result in the greatest reduction in size. This use case stands in contrast to, say, a compressed backup file which might only be accessed in a disaster recovery scenario or never accessed at all, making size more important than speed. Both the xz and zstd utilities have some built-in support for multi-threading, but the default is to run in a single thread. While xz can use multiple threads for decompression (but only if the file was compressed in multi-thread mode), the reference zstd utility can only use more than one thread for compression, not decompression. There are many other methods of lossless compression that have been developed over the decades, but I believe these are the ones you are most likely to encounter in the world of UNIX-like systems. This is a personal opinion, and others might choose a different set. As mentioned, it can be tough for a new method to gain popularity and 35-year-old DEFLATE is still probably the most commonly used despite not being the fastest or offering the greatest reduction in size. Even systems like FreeBSD, NetBSD, and OpenBSD that do not like to include GNU tools supported it by developing their own version of gzip based on the permissively-licensed zlib library. Technically, the LZW method used by the compress utility is still standardized by POSIX, so one might expect it to have the widest support. However, aggressive patent enforcement discouraged adoption, especially by Free and Open Source Software systems—even though the patent has expired, it is still out of favor compared to DEFLATE. For this reason, I feel justified in calling it a curio. References: Eighth Edition UNIX pack.c https://www.tuhs.org/cgi-bin/utree.pl?file=V8/usr/src/cmd/pack/pack.c 2.9BSD compact.c https://www.tuhs.org/cgi-bin/utree.pl?file=2.9BSD/usr/src/ucb/compact/compact.c Compress specification https://pubs.opengroup.org/onlinepubs/009695399/utilities/compress.html Uncompress specification https://pubs.opengroup.org/onlinepubs/009695399/utilities/uncompress.html GNU Gzip manual https://www.gnu.org/software/gzip/manual/gzip.html RFC 1951: DEFLATE Compressed Data Format Specification version 1.3 https://tools.ietf.org/html/rfc1951 History of Lossless Data Compression Algorithms: The Rise of Deflate https://ethw.org/History_of_Lossless_Data_Compression_Algorithms#The_Rise_of_Deflate bzip2 https://en.wikipedia.org/wiki/Bzip2 XZ Utils https://en.wikipedia.org/wiki/XZ_Utils 2.6.38 merge window part 2 https://lwn.net/Articles/423541/ zstd https://en.wikipedia.org/wiki/Zstd Appendix The table below demonstrates the results of compressing different types of data using tools described in this episode. While not totally rigorous, I did run each compression and decompression multiple times to ensure I was getting consistent results. The laptop I used has an Intel Core i5-6200U CPU running at 2.30GHz, and the system had at least 5 GB of free memory for each run. While this processor has two cores and can run four simultaneous threads, all utilities were run single-threaded. The term "best" means the highest level of compression available (the exact level used is shown). For bzip2 , the default is the best. For zstd , "best" is -19, which is the highest "normal" level, but "ultra" levels that are even higher also exist. Ratios are the percentage of the original size that the file was reduced to (other sources might instead express the compression ratio as the reduction in size achieved). In all results, smaller numbers are better. ┌────────────────────────────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┐ │ │ gzip │ gzip │ bzip2 │ xz │ xz │ zstd │ zstd │ │ │(default -6) │ (best -9) │ (-9) │(default -6) │ (best -9) │(default -3) │ (best -19) │ ├──────────────┬─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │ │Size (ratio) │ 22,036,508 │ 21,891,623 │ 15,795,698 │ 13,487,768 │ 12,938,464 │ 20,454,657 │ 13,709,078 │ │ │ │ (24%) │ (24%) │ (17%) │ (15%) │ (14%) │ (23%) │ (15%) │ │English Text ├─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │(90,532,092 │Compression │ 4.8s │ 7.6s │ 8.5s │ 49.8s │ 58.8s │ 0.6s │ 65.2s │ │bytes │time │ │ │ │ │ │ │ │ │uncompressed) ├─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │ │Decompression│ 0.7s │ 0.8s │ 3.7s │ 1.2s │ 1.2s │ 0.4s │ 0.4s │ │ │time │ │ │ │ │ │ │ │ ├──────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │ │Size (ratio) │ 125,291,122 │ 124,189,544 │ 98,016,512 │ 84,882,492 │ 81,954,344 │ 120,604,855 │ 87,298,645 │ │ │ │ (21%) │ (21%) │ (17%) │ (14%) │ (14%) │ (20%) │ (15%) │ │Source Code ├─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │(590,008,320 │Compression │ 22.0s │ 39.3s │ 54.8s │ 241s │ 298s │ 3.7s │ 348s │ │bytes │time │ │ │ │ │ │ │ │ │uncompressed) ├─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │ │Decompression│ 5.1s │ 5.1s │ 20.3s │ 8.1s │ 7.8s │ 2.4s │ 2.4s │ │ │time │ │ │ │ │ │ │ │ ├──────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │ │Size (ratio) │ 32,830,905 │ 32,371,241 │ 26,856,579 │ 20,717,288 │ 20,352,880 │ 28,538,810 │ 23,154,582 │ │ │ │ (19%) │ (19%) │ (16%) │ (12%) │ (12%) │ (17%) │ (13%) │ │Binary Program├─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │(171,972,264 │Compression │ 6.4s │ 22.4s │ 18.6s │ 62.2s │ 67.8s │ 0.8s │ 111s │ │bytes │time │ │ │ │ │ │ │ │ │uncompressed) ├─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │ │Decompression│ 1.5s │ 1.5s │ 5.6s │ 2.3s │ 2.3s │ 0.7s │ 0.7s │ │ │time │ │ │ │ │ │ │ │ ├──────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │ │Size (ratio) │ 146,397,772 │ 146,397,757 │ 144,485,451 │ 131,950,232 │ 130,926,780 │ 147,154,979 │ 145,703,840 │ │ │ │ (89%) │ (89%) │ (88%) │ (80%) │ (80%) │ (90%) │ (89%) │ │WAVE Audio ├─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │(164,396,302 │Compression │ 9.2s │ 9.2s │ 25.1s │ 70.4s │ 97.7s │ 0.7s │ 58.3s │ │bytes │time │ │ │ │ │ │ │ │ │uncompressed) ├─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │ │Decompression│ 2.0s │ 2.0s │ 13.5s │ 12.2s │ 12.1s │ 0.6s │ 0.8s │ │ │time │ │ │ │ │ │ │ │ ├──────────────┴─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ │ │ gzip │ gzip │ bzip2 │ xz │ xz │ zstd │ zstd │ │ │(default -6) │ (best -9) │ (-9) │(default -6) │ (best -9) │(default -3) │ (best -19) │ └────────────────────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┘ English text consists of Titles 1 through 10 of the 2020 U.S. Code of Federal Regulations . Source code consists of a tar file containing the Linux kernel source, version 4.0. Binary program consists of an ELF-format executable of the pandoc application, version 2.17.1.1 found on Debian 12. Audio consists of a 24-bit Signed Integer PCM WAVE file with 2 channels at 44.1kHz, about 10:21 in length. For comparison, the audio-specific flac lossless compression utility reduced this file to 97,962,711 bytes (60%) in 2.6 seconds at the default (-5) level and to 97,714,876 bytes (59%) in 5.4 seconds at the highest (-8) level. Provide feedback on this episode.

The NetBSD/FreeBSD Merge announcement, the rise and fall of SPARC, GhoseBSD 26.2 and more... NOTES This episode of BSDNow is brought to you by Tarsnap and the BSDNow Patreon Headlines NetBSD/FreeBSD will not merge, November 1993 announcement Rise and Fall of SPARC: Why No One Misses It News Roundup Help needed testing GhostBSD 26.2 Redundant DHCP server and DNS Resolver using OpenBSD and FreeBSD Universities And In house Tech Beating my head on OpenVPN Tarsnap This weeks episode of BSDNow was sponsored by our friends at Tarsnap, the only secure online backup you can trust your data to. Even paranoids need backups. Feedback/Questions Paul - Feedback Send questions, comments, show ideas/topics, or stories you want mentioned on the show to feedback@bsdnow.tv Join us and other BSD Fans in our BSD Now Telegram channel

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!On the product side, everyone is getting Computer - Perplexity, Manus, Cursor, and so on. Meanwhile on the research side, agentic evals like TerminalBench and GDPVal are also assuming computer (Harbor). On both ends, the consolidating LLM OS stack has become a standard toolkit, and Daytona is one of a small set of AI Infra companies that are booming because of it.“The end of localhost” has been Ivan Burazin's obsession for more than a decade.Something that is all too familiar…Long before agents became the default way people talked about software development, Ivan was already chasing the idea that development should not depend on a fragile local machine. CodeAnywhere, one of the first browser-based IDEs, was an early attempt at that future: move the development environment into the cloud, make setup reproducible, and free developers from the endless “works on my machine” tax.The thesis was directionally right, but the market wasn't ready yet.However, agents changed that. They do not care about a laptop, desk setup, or favorite editor. They need a computer they can access through an API: something stateful enough to keep working, fast enough to spin up instantly, flexible enough to resize, isolated enough to be safe, and composable enough to run the messy real-world workflows that real software engineering actually requires.Daytona isn't just selling “sandboxes” in the narrow code-execution sense. It is the latest version of Ivan's original localhost thesis.In this episode, Daytona's CEO joins swyx to explain why AI agents need more than code execution boxes: they need composable computers, stateful sandboxes, instant startup, dynamic resources, and infrastructure that can survive workloads going from zero to 100,000 CPUs.We go deep on the new agent compute market: Daytona's hard pivot from human dev environments to AI sandboxes, the New Year's Eve MVP that customers begged for, why Daytona runs on bare metal with its own scheduler, how one customer runs almost 850,000 sandboxes a day, and why RL/eval workloads went from 0% to roughly 50% of usage in just months. Ivan also explains why agents need Windows and macOS machines, why CLI may matter more than MCP, why Kubernetes is painful for this workload, and why the future AI cloud may look more like Stripe than AWS.We discuss:* How Daytona grew out of CodeAnywhere, Shift, and the “end of localhost” thesis* Why Daytona pivoted from human dev environments to AI sandboxes* Why agents need composable computers instead of disposable code execution boxes* The New Year's Eve MVP that customers chased API keys for* Why Daytona chose bare metal, stateful snapshots, and its own scheduler* How Daytona spins up one sandbox in ~60ms and 50,000 sandboxes in ~75 seconds* Why Daytona's biggest customer runs ~850,000 sandboxes a day* How RL/eval workloads create zero-to-100,000 CPU spikes* Why RL workloads went from 0% to roughly 50% of Daytona usage* Why customers compare Daytona against EKS/GKS and say they're “never going back”* Why every AI agent may need a computer, including Windows and macOS environments* The Apple licensing constraints that make macOS sandboxes hard* Why CLI gives agents more power than MCP* How open source helps agents integrate Daytona* Why agent-generated PRs may break today's CI/CD assumptions* Why AI SaaS companies reselling tokens may face a cold shower* Why the AI cloud may look more like Stripe than AWSIvan Burazin* LinkedIn: https://www.linkedin.com/in/ivanburazin* X: https://x.com/ivanburazinDaytona* Website: https://www.daytona.io* X: https://x.com/daytonaioTimestamps* 00:00:00 Hook* 00:01:12 Introduction* 00:03:15 CodeAnywhere, Shift, and the end of localhost* 00:05:58 What Daytona is: composable computers for AI agents* 00:08:07 The pivot from dev environments to AI sandboxes* 00:10:17 The New Year's Eve MVP and customers begging for API keys* 00:12:56 Bare metal, stateful sandboxes, and Daytona's scheduler* 00:17:28 60ms startup, 50,000 sandboxes, and 850K daily runs* 00:21:53 Spiky RL/eval workloads and the new agent infra problem* 00:28:12 RL workloads, Kubernetes pain, and dynamic resizing* 00:33:31 Why every AI agent needs a computer* 00:38:48 macOS sandboxes and Apple's licensing problem* 00:44:28 Why CLI may matter more than MCP* 00:48:11 Open source, GitHub stars, and agent integration* 00:53:11 Git, CI/CD, and agent collaboration bottlenecks* 00:58:15 Founder life and building a 25-person infra company* 01:02:44 AI SaaS, token resale, and API-first business models* 01:06:10 GPU sandboxes, data centers, and compute growth* 01:09:48 Why the AI cloud may look more like Stripe than AWS* 01:11:26 Closing thoughtsTranscriptIntroduction: Daytona, CodeAnywhere, and the End of LocalhostSwyx [00:00:02]: Okay, we're in the studio with Ivan Burazin, CEO of Daytona. Welcome.Ivan [00:00:07]: Thanks for having me, man.Swyx [00:00:08]: Ivan, you and I go back.Ivan [00:00:10]: Way back.Swyx [00:00:11]: How I don't even know how, you found, did you reach out or, for Shift.Ivan [00:00:17]: I reached out to you. The reason was you - we were just - we were thinking about I was one of the co-founders of CodeAnywhere, the first browser-based IDE, and so we were thinking a long time of, localhost should die. And you had this article.Swyx [00:00:29]: End of localhost.Ivan [00:00:30]: Then I reached out to you because of that, and then we talked, and I was actually at a different job and learning about I was the head of, developer experience, and you were quite well-versed in that, and I actually reached out to you, among other people, how do we go about that? What are the key things and whatnot at this point in time? And you were nice enough to take the call, and I remember I was late on your call with you.Swyx [00:00:51]: I don't remember.Ivan [00:00:52]: I remember because I was with my then I'm thinking of a girlfriend or wife at that point in time, I'm not sure. It's the same person, so that's great, and I was late ‘cause we were, in, Italy on, vacation, and then I was late for something. I felt so bad, and you were so nice to be, good about.Swyx [00:01:10]: The reason I'm nice is because I'm also late to other people, so it's like, who's, who's without sin here, yeah, so I have to, for those who don't know, InfoBip Shift, there's this whole thing that, you did in the past, and, and that was basically one of the inspirations for me starting AI Engineer, which is like, I have to thank you for giving me that push to be like, “Oh, you can, you can build and sell conferences?”Ivan [00:01:34]: I remember you asked you asked me at the beginning to give me advisory shares, and I was so focused on what we were doing, I said no, and I should've took the advisory shares. So I'm sorry, dude. But anyway.Swyx [00:01:43]: We're not, we're not venture backed.Ivan [00:01:44]: No, it doesn't matter.Swyx [00:01:45]: It's Yeah, anyway, so I think what's impressive about you is that CodeAnywhere is the thing that you've been trying to build, and, you kind of put it on hold and then came back after InfoBip. Just give us the story, do you - the story and the origin story, going into Daytona.From CodeAnywhere and Shift to DaytonaIvan [00:02:05]: Sure. Like, really way back, me and my co-founder have been together. I say this, I've said this multiple times, it's like we were married and divorced and married. Some people actually ask me is my co-founder my partner. they thought it literally. It's not literally, but we have done multiple companies together, and to your point, we had this shift where we went from the CodeAnywhere to the conference called Shift, and then back to, Daytona. We originally started stacking servers, doing like virtualization in the early 2000s and, routers and doing basically all these things, at a foundational level, and that was a services company which we sold to focus on what my co-founder actually invented, which was the very first browser-based IDE, right, I say the first. Before us was actually Heroku. They did it for a very short time until they became Heroku. But outside of them, we were the only one, and it was called.Swyx [00:02:55]: There was Cloud9.Ivan [00:02:57]: Cloud9 came out slightly after us. There was Replit, which came out when we stopped doing it, Replit came out, and they have been successful since then, which is great. There was Nitrous.io. There was quite a few that existed at the time, but it was like too early. But the interesting part is that we, at that point in time, because there was no VS Code, there was no Kubernetes, and Docker had just started when we Or I'm not sure if it was even public at that point in time. And so we had to build everything to the whole stack ourselves and that was the key learning that we brought into and that we've been using in Daytona today. So it was super early. There's about 3 million people used CodeAnywhere. It was slightly, it was angel-backed more than venture-backed. We ended up paying everyone back because it didn't have that sort of scale. But, three years ago, we started something similar with Daytona, which is not what we are today, but it was automating dev environments for human engineers, the basically the underlying stack of CodeAnywhere. And then we did a hard pivot last January to sandboxes. And so here we are.Swyx [00:04:01]: Historic pivot, yeah, and, it's one of those things where, I had independently invested in CodeAnywhere, but also in E2B, and then both of you pivoted into the same thing, and I'm like, “F**k.”Ivan [00:04:12]: You invested, you invested in Daytona. You invested in Daytona. But you were the first If we had not got your check, we wouldn't have done it.Swyx [00:04:18]: No way.Ivan [00:04:19]: No, it was like, “We have to get him on board first,” and you were that kicker that we, that got us off the ground.Swyx [00:04:23]: No, because you were putting me on your pitch deck, man. I was like, “Man, this is like a good trip if I don't invest.”Ivan [00:04:29]: That's because it was your quote. It's like we.Swyx [00:04:30]: Yeah. It's the end of localhost.Ivan [00:04:31]: Did a bunch of research about end of localhost and who was interested in that,.Swyx [00:04:34]: No, that's like, I put, I wrote that blog post, and every single company in that field reached out to me, and then every VC who was receiving those pitches then also had to call me and, talk it, talk through it with me.Ivan [00:04:47]: It's finally happening though.Swyx [00:04:48]: It was really super interesting.Ivan [00:04:48]: It's finally happening.Swyx [00:04:49]: It's finally happening.Ivan [00:04:49]: Yeah, it's finally.Swyx [00:04:49]: It's finally happening, with maybe sort of non-human users. Yeah, so what is Daytona today? Let's get like a quick description. I'm wearing the shirt.What Daytona Is Today: Composable Computers for AI AgentsIvan [00:04:58]: You're wearing the shirt. Yes,.Swyx [00:04:59]: It says, I think your branding is very good. Like, it's very consistent. It runs AI code. Like, it cannot be simpler.Ivan [00:05:05]: Exactly, but we're gonna probably have to change that.Swyx [00:05:07]: Oh, s**t.Ivan [00:05:07]: It's also a subset of what we do. Unfortunately, we really love this, Run AI Code is super simple. People interpret it different ways. I think we've given out 5,000, 6,000 of these shirts. People wear them with pride because it doesn't really market about us.Swyx [00:05:21]: Yeah, Daytona's on the back.Ivan [00:05:22]: It markets the back. It markets to the person itself, so I think we did a really good job on that one. But it is also a subset of what we do, because people, when they think about Run AI Code, they just think about these small, let's call it isolates, code execution boxes that, you send some code, you get an output. Whereas what Daytona is today is essentially composable computers for AI agents. It is, the market calls them sandboxes which can be misleading.Swyx [00:05:44]: All these things. All these things on.Ivan [00:05:45]: Yeah, exactly, ‘cause it can be misleading ‘cause people usually think about sandboxes as a demo or a test environment versus a production-grade environment. But what Daytona does, if you think of the laptop that you have in front of you or the computer that's over there, or, my wife is an architect, so she has like a Windows with a 3D graphics card inside to do 3D rendering. Like, as humans, we have different computers or different compositions of computers. And our belief is strongly that agents today and going forward will need all these different compositions of computers to do different types of tasks. And so we offer that basically through an API.Swyx [00:06:19]: Yeah, to give people - I'm trying to sort of front-load all the aha moments or the wow moments so that people can, stay engaged and click like and subscribe. the market is exploding, right? Like, you have been reporting 74% month-on-month growth, and it also, it's just been growing for a while. Like, it's been going like this. And every single - It's not just you guys. It's every single.Ivan [00:06:41]: Everyone, yeah.Swyx [00:06:42]: Sort of, compute provider. I don't know if you agree with me saying compute provider or not.Ivan [00:06:48]: It's fine.Swyx [00:06:48]: Yeah. So like organically PLG-driven growth, but also enterprise is doing super well, I think I wanna rewind to January of last year when you did the pivot. Like, so you obviously called this market early, and you were positioned for it, and you are now one of the market leaders. But what was the insight that made you do the pivot?The Pivot: From Human Dev Environments to Agent SandboxesIvan [00:07:06]: The insight that made us do this pivot is the quarter before that, so end of 2024, when we had - Basically, we did a demo with - I don't I think we discussed this as well, Devin was not public. You actually gave me access to Devin at that time. So Devin.Swyx [00:07:25]: I did?Ivan [00:07:26]: Yeah, you gave me access.Swyx [00:07:26]: I don't think I was supposed.Ivan [00:07:27]: Yeah, exactly.Swyx [00:07:28]: Yeah, I.Ivan [00:07:28]: So it doesn't matter. You.Swyx [00:07:29]: Yeah. I gave like three friends access.Ivan [00:07:31]: Yeah, or it was a call and you showed it to me. It doesn't matter. but OpenDevin was available, which is now called OpenHands. And so we're like, “Oh, this seems to be a thing. This is not public. Let's take our for human automation of dev environments and take, OpenDevin and launch that as a SaaS.” And we did that. Not very many people signed up and used it, but a lot of people reached out that were building agents, and they were like, “Hey, my agent needs a compute sandbox runtime,” whatever you wanna call it. I forgot what it was called at that point. And then we were like, “Oh, amazing. This is a new market. Here is our infrastructure. Here's our product, and go.” And what we found really fast, soon, was that people did not like what we had built. It didn't work. And I remember talking to people at the beginning when we're doing this, the sandbox we're building for agents. People were like, “Oh, why is it different? It's the same thing. We have like EC2, we have VMs, we have all these things.” But we saw that everyone we gave it to, it was like 20, 30 people, they all said, “No.” Like, “This is not what we need. This sort of breaks.” And basically, me and my co-founder not knowing a lot about - ‘cause we're infra people. We're not AI people. So I basically took it upon myself to like watch every single podcast that exists, including all of, all of these and all that, and sort of get up to date, read all the blogs, like get, understand what's going on.Swyx [00:08:45]: Do you wanna shout out who else was useful, just in case people are also looking.Ivan [00:08:49]: Generally we -, I looked at There's a few of podcast, different segments and different types. So there's you guys, No Priors, Bill Gurley's was great while.Swyx [00:09:04]: VG2, yeah.Ivan [00:09:05]: Yeah, while it was around. So there's a few. 20VC is interesting from a different dynamic, and some are different dynamic. But there was, also Red Points.Swyx [00:09:14]: We're not really about the compute market.Ivan [00:09:15]: It was also already - Sorry?Swyx [00:09:16]: You're, you want - You're looking at the agent infra market.Ivan [00:09:19]: I was looking at the agent market and the AI market in general and sort of understanding who are the players, what the perception, and how that goes. And like obviously you complement this with like going to conferences, going to events, going to meetups, reading white papers, like doing all the things that you have to do to understand what's happening. And so when we figured, when we sort of had an idea of what we had to build, literally over the New Year's Eve, literally on New Year's Eve, I half vibe coded the first MVP, first minimal viable product of what Daytona is today. And I went to sleep at like 3:00 AM or something like that. I was doing - I just put my like baby daughter and wife to sleep and, Happy New Year's, and go back to just, doing this. And I sent it to my co-founder, my CTO, and he saw it in the morning. He's like, “This is absolute garbage.” “Do not show this to anybody at all, but the idea is good.” And so he took two weeks, and he rebuilt it.Swyx [00:10:09]: Did it like look like that? Listen, I - It was rough idea.Ivan [00:10:12]: Oh, not even, not even close. Like it was it was way worse. But it was like a very - It was a simplistic view of what it should be. Like, it worked, but it was not ideal. And so he went, we went down the whole, which is his job as CTO, to go, and he came back with this version. We then called all the people that had said like, “This is garbage,” a quarter ago. And we set up these calls, and we gave it to - We just demoed it to everyone. And all the calls went long, every single one. They were 15-minute calls, and they all went to like 25, 30 minutes or whatnot. And everyone said, “We need, we want access.” There was no login, just an API key, ‘cause it was just a beta or an alpha. And they said, “Oh, we want access.” And we're like, “Sure, yeah. Okay, thank you very much.” But after like the next day, if we'd not send it, every single one, like every call that we did, everyone came back, “Where is my API key?” Like everyone wanted it. We're like, “S**t.” Like this is it. Like I've never felt So one, the understanding to your point was like most people thought it was the same infrastructure for humans and agents. We understood a quarter ago it's not. We just didn't know what was the right primitive. And then when we came, and we can talk about what that is, and we gave it to these people, I've never seen, I've never experienced - I've done multiple companies in my life. I've never experienced this, that people literally call you if you do not give them access. Like they want access right now. And so it's like, okay, they don't want this. the thing that they want doesn't seem to exist, or they have not found it, and they really want what we want. And then when we understood that we're onto something, and then when you think about the size of the market, like the market for human engineers and enterprise is a very large market, so think GitLab or whatnot. But the market for every single agent that will exist ever in the future is just like, what is that market? How big is that? And we're like, “We are all in on this.” And so that is where we made sort of the cut between the old product and the new one.Bare Metal, Stateful Sandboxes, and the Lambda + EC2 ModelSwyx [00:12:02]: Yeah. But it wasn't composable at the time?Ivan [00:12:05]: It was very - It was basically just a Linux box that you could change, that you could define number of CPUs, disk, and RAM. Like that is what you could do, but you couldn't have multiple operating systems, you couldn't resize it on the fly, you couldn't add a GPU, you couldn't do like all the things. It was just the, just the first sort of variation of that, yeah.Swyx [00:12:22]: Was it bare metal from the start?Ivan [00:12:24]: It was bare metal from the start. And so the interesting thing that we thought about right away, so our.Swyx [00:12:29]: Which, give people the background, what is the normal path?Ivan [00:12:32]: Yeah, so, basically most providers run this on top of VMs. And also.Swyx [00:12:37]: Firecracker.Ivan [00:12:38]: Yeah, they run on Firecracker and VM. And so we also fire - We can get - We have multiple isolation layers and we can do that. But the common way to do it is that they, one, that the state of the machine, or the hard disk is not part of the sandbox itself. And the other thing is they're not meant to last forever. So most of them are preemptible, like they can There's a time that they can live. And so our thought was when we were going into this is, agents will be like humans in the sense of you don't want your laptop to be shut down until you're done with work. Like, and you want to close the lid and open the lid, it's the same state. So you - Agents would want that, like the pause and come back. They want those two things. But also agents really want speed, right? Can they get it? So when we thought about it's like we need something insanely fast, how to make it fast, how to make it long-running, and stateful. And so those two things, it's like combining a Lambda and an EC2, right? Those two things together. And so we didn't have an idea how others did it, ‘cause we didn't know too that there was a market around this. It was more like, okay, this is what we need, what they need. And we looked at Kubernetes, it wasn't wasn't good enough for that. We looked at Nomad, it didn't enable that. And so our history in rewriting our own scheduler at CodeAnywhere is basically what my CTO came up with. Like, he's like, “Oh, the learnings from there,” and he brought it. And the funny thing is, our third co-founder, when he saw it, he's like, “Dude, what is this? This is like 2008.” Like, we went back in time, and he's like, “Exactly.” And so the reason why Daytona is like super fast, and you see this on benchmarks, is we essentially, we run on bare metal. We have our own scheduler, we use the underlying, disk, CPU, and RAM of the underlying machine, which means your IOPS are insanely fast because there's no, there's no network between an EBS or something like that. But also the snapshot, the point in time, the templates, are also preloaded on the bare metal machines. So when you fire off a sandbox from a template or a snapshot, you're essentially directed to the bare metal machine where that snapshot is based on that NVMe drive, and then it literally just turns on that machine, and it's local. There's no network latency, anything on there. And so that is sort of the specificities that we, when we're thinking from first principles, what a computer would look like for an agent, that is what we came up with, and that's what we created.Benchmarks, 60ms Startup, and 50,000 SandboxesSwyx [00:15:02]: Yeah. I should maybe, I don't know if you endorse this, but there's someone that does compute SDK, you guys do very well on there, with like the TTI, right? I. is this a, is this a is this a relevant benchmark for you guys? I don't know.Ivan [00:15:16]: I don't know, and it changes every day. So today RKL is.Swyx [00:15:18]: I don't know what RKL is. Never heard of it.Ivan [00:15:20]: Yeah. RK, yeah, so it is there.Swyx [00:15:22]: You are, at least a third of the next tier of performance, and then, there's a lot of other better-known names that are very slow to start.Ivan [00:15:31]: Yeah. We've been the number one by far for a long time, and now there's different, there's different definitions also of sandboxes, different isolation patterns, different other things. So RKL runs it literally on the S3, the data, so it's very different, and they spin up a sandbox, spin up a container for that, so it's a different type of thing. So the definition of a sandbox is something that we can all, we all need to get along with. But yeah, we're insanely fast on getting these things, up and running. And so you can see even there that it's a zero point 0.10 to 0.11, so.Swyx [00:16:03]: Close enough. Yeah. what else do you need, right?Ivan [00:16:05]: Yeah. So the benchmarks itself, so, in this, in I don't think the benchmarks equate to market ownership or revenue or anything like that. and I've seen this with multiple benchmarks, not just in sandboxes, but in general benchmarks around.Swyx [00:16:20]: It's table stakes. It's just like.Ivan [00:16:21]: Exactly. But it doesn't hurt.Swyx [00:16:22]: Just roughly check.Ivan [00:16:22]: Like you definitely have to be up there and you have to be competing so that people know that, oh, this is definitely one of the top. Because this is only one dimension of what customers look for. There's other things like how many can you spin up consecutively? There's a feature set, there's support, there's like all different things that people look at, but you definitely have to be there, on the benchmarks.Swyx [00:16:40]: How many people do people spin up consecutively?Ivan [00:16:43]: So we have.Swyx [00:16:43]: Or concurrently, is the Concurrency, right?Ivan [00:16:45]: There's three metrics that we look at. And so one is like time to spin up one, and so our time to spin up one is 60 milliseconds with network latency. So request, spin up, reply, 60, the whole thing, 60 milliseconds. That is one. But if you wanna spin up 50,000 at once, we are now at about 75 seconds. So it takes about 75 seconds to spin up concurrently 50,000. Some others, there's public data around this, like take 2,000 seconds, which is 30 minutes. Like there's different variations of that. And then there is the so it is speed of one, speed of like multiple, and then how many can you consistently have up and running. And so we basically have right now no limit to how much we can add because we basically own our own metal. But the biggest customer of ours does like about 850,000 every single day is sort of where they're, where they're just shy of a million every single day that they're running, we do have a request for half a million concurrent, which is literally half a million CPUs somewhere running. So that's an interesting.Swyx [00:17:44]: They pay by like vCPU seconds.Ivan [00:17:47]: By seconds, yeah.Swyx [00:17:47]: Or whatever. Yeah. Okay, and so and then, and the other thing is, the sleeping and the resuming, ‘cause it's all the stateful resumption of all these things, how, what kind of workload are people putting through this, right? Like how is it Do we measure by gigabytes in memory, gigabytes in storage? I don't In like network attached storage. I, what are the costly ones of, out of all these features?Workload Economics: CPU, RAM, Network, and StorageIvan [00:18:15]: The most expensive thing are CPU.Swyx [00:18:18]: Okay. Yeah, of course.Ivan [00:18:18]: The second one, yeah Then it's RAM, then it's disk. We actually don't charge.Swyx [00:18:22]: Which is snapshotting, right?Ivan [00:18:23]: No, it's actually the, snapshotting's part of it, but basically the size of your hard disk, of your machine. So do you have 10 gigabytes, do you have 20, do you have 50, do you have whatever? And then the transference of that. Right now, currently we don't charge for, network at all at Polychron.Swyx [00:18:37]: Oh, you gotta, yeah, you gotta fix.Ivan [00:18:38]: Yeah. It is very much a it's a larger and larger part of our bill, so we're working around, that part there. Obviously, that is the least, expensive, so the hard disk is the least expensive, so it's basically CPU, RAM, for us network, ‘cause we don't charge the customer, and then hard disk, is how it's split up. But there's also different types of workloads, so we basically split it up into two types of workloads in Daytona. One is what we call background agents or long-running agents. and the other is, basically RLs and evals, which I put sort of together. And so they have very different patterns of usage, and if you look at the usage of a background And I'll just name names of companies, not specifically.Background Agents vs. RL/Evals: Two Usage ShapesSwyx [00:19:21]: Yeah, open, all hands.Ivan [00:19:23]: Yeah. So like a background agent's a Cognition, a Lovable, a like all these things are Harvey. These are all long-running, background agents. And so if you look at their usage patterns, their usage patterns are similar to human, which is like follow the sun. Basically, the usage patterns of that is like noon is probably the highest, and the midnight is the lowest, and then weekends are lower. weekday is higher.Swyx [00:19:42]: Yeah, that's a fun question. How global is it? Is it very US-centric or?Ivan [00:19:46]: The US is a large part, but we have currently, we have Asia, Europe, and the US regions.Swyx [00:19:52]: So it's quite global.Ivan [00:19:53]: Yeah, it's quite global. We have it all over. It's interesting that our I talked to you a bit about this. Our number one city by user.Swyx [00:20:01]: Hmm.Ivan [00:20:02]: Is Singapore.Swyx [00:20:04]: Oh, wow. Amazing.Ivan [00:20:05]: Which is an interesting one, right? Not by revenue, just by just like by individual head count.Swyx [00:20:09]: Really?Ivan [00:20:09]: Just like an interesting thing.Swyx [00:20:10]: Singapore is, Singapore is weirdly high in the adoption charts of AI for the population. It's like an, seven, eight million population. And it's like keeps showing up.Ivan [00:20:20]: No, it's quite interesting. We were quite shocked, and I was like, “Oh, this is interesting.” And also one that's up there.Swyx [00:20:24]: There's a reason I'm doing AI using Singapore. it's because I'm from there.Ivan [00:20:27]: We're there. We're gonna, we're gonna be there as well. and it's interesting that Japan is in the top or like Tokyo's in the top, which is in all the tech cycles it has never been. It has never been, so it's quite interesting that they're.Swyx [00:20:39]: I think the Japanese just love AI. Yeah. It's that, and then it's Brazil. That's it.Ivan [00:20:44]: Brazil has always been in.Swyx [00:20:45]: I think.Ivan [00:20:46]: Even when I look, if you look at like GitHub's data and ask historically with CodeAnywhere, it was always like US, Western Europe, and then you'd have like India, Brazil, China, like that would be there. But like Singapore was not in, specifically Japan was never in sort of that top, that top.Swyx [00:21:01]: Yeah. Weird pockets.Ivan [00:21:01]: Weird. Yeah, so it's very global.Swyx [00:21:02]: Okay, so actually that, but that's helps you to distribute your load through, all time?Ivan [00:21:08]: The interesting thing is like we have those kind of loads, but if you look at the researcher loads, they're quite different. So what they are is like if you give them concurrency of 10,000 or 50,000 or 100,000 CPUs at ARMb, when they fire off a run, it's just 100%. And then it just runs, and then it stops. So it's very, the usage pattern is squares basically, right? And it's also not follow the sun, because people will fire it off at midnight before they go to sleep but then wake up and so it's very unpredictable, so you don't know where that is. So the shapes of the usage are quite different than we have had before. And also what's interesting is when it's sort of a follow the sun, even if you have a high growth company, you can sort of predict your usage patterns and have enough capacity for that, because it's sort of, it grows in a, in a way you can project. When you have companies doing sort of like evals and RL, they're super spiky. So they're gonna come in, it's like, “We're gonna use nothing, then can we have 100,000?” Right? And then go back down. And then 100,000, go back down. So it's very different, right? And.Swyx [00:22:09]: Do you want to lock them into commits so.Ivan [00:22:11]: Yeah, we do.Swyx [00:22:12]: Yeah, okay.Ivan [00:22:12]: We so we have to lock them into some sort of commits to have that capacity, because we have to have, basically we have to have the capacity for peak. Right? And so right now, Daytona's mean utilization is 15%, 1-5.Swyx [00:22:25]: Oh my God.Ivan [00:22:26]: So it's very low.Swyx [00:22:27]: Because it's very spiky.Ivan [00:22:27]: It's very spiky, but we get up to 90%. so we have these things. And so what we're, what we're looking at right now as a company is similar to Cloudflare where you can like geo move things around, but that works really well for basically the background agent where it's follow the sun. But this, it's not. Like it's a very different shape. Obviously with scale you figure these things out, but that's an interesting new problem that we have, as a compute provider in the agent space. And when we were doing the conference recently, and so we talked to like Nikita from Neon and.Swyx [00:22:57]: I should bring it up.Ivan [00:22:58]: Parag from Parallel and whatnot, everyone has the same problem. Whereas the usage is super spiky, and this is something that has not happened before, that you have these types of like it was always, it the amplitudes were not this high, right? So it's quite interesting use case and problem solve.Compute Conference and Spiky Agent InfrastructureSwyx [00:23:12]: Yeah, I don't know if we're gonna bring this up again, but let's just talk about the conference, you had like 1,000 something people at the Warriors game, at the Sorry, where is it? What's.Ivan [00:23:22]: Chase Center.Swyx [00:23:23]: Chase Center.Ivan [00:23:23]: Chase Center.Swyx [00:23:24]: I went. It was, it was very impressive. Obviously, you can, how to throw a conference, what did you learn? you put, you pulled together all these impressive names.Ivan [00:23:33]: What I.Swyx [00:23:34]: What were you looking for?Ivan [00:23:35]: My thesis behind the Compute Conference was let's bring together people that are building infrastructure for AI agents. Because when I think of what we're building, it is the agent is the primary user, what are the ergonomics and usage patterns of agents, and so we can do that. And what I found, this was a theory, it wasn't proven, is that we all have these problems, as I touched onto. And I was, as I was talking on stage, it was like we all have the same underlying infra problems, which is this spiky workloads, unpredictable workloads that we've never had before, in human, compute or human infrastructure. And it's, again, it's the same when I was talking to Parag or when I was talking.Swyx [00:24:20]: Lynn. Nikita.Ivan [00:24:21]: Lynn, Nikita. Lynn especially, I was talking to her the other day as well. Like the It is a very interesting type of problem to solve because I can touch on Cloudflare because there's a lot of like talk about that recently as to how they solve that, which is they have a bunch of geos, and basically, as users work in different places, and depending on your tier, they can move you around the geos. And so that how, that's how they get the higher utilization. But you can sort of predict these, and it's If it's something in You'll rarely get a spike that is 10 orders of magnitude. Like you'll get a like let's say one of your customers has some like an exponential curve. What is that to I'm using Cloudflare as an example. 10%, 20%, whatever it is. I don't, I don't have this data, I'm just assessing. It's surely not 10x, right? It's surely not something there. And so how do you go out and solve this problem? And we're all solving this in different ways. So we have.Swyx [00:25:11]: She also has the same thing.Ivan [00:25:12]: Yeah, I know specifically that like Neon had that issue as well. Like how are we solving these spiky loads and things like that ‘cause we talked about it. And so the interesting thing for me to actually internalize was, yes, everyone that's building for agents first is going through this, and we're all solving similar problems, which is quite.Swyx [00:25:28]: Let me let me double-click on this. Okay. So for example, Neon, I happen to know that they're very sort of S3 oriented, right? so they're just like fully bet on S3. And you get to benefit from S3's distribution and infrastructure. So I would imagine that Neon doesn't have to care, whereas Lynn maybe has to care a bit more because obviously she's doing GPU inference. And, for listeners, we did an episode with her, one and a half years ago. And you have to care. But like, right?Ivan [00:25:54]: Parag cares for sure, and Nikita.Swyx [00:25:58]: And Parag is C of, Parallel.Ivan [00:25:59]: Parallel, yeah.Swyx [00:26:00]: Former CTO of Twitter.Ivan [00:26:01]: Twitter, yeah.Swyx [00:26:02]: They are the search.Ivan [00:26:03]: Yeah, they're search, yeah.Swyx [00:26:03]: I You and I know but the listeners don't know.Ivan [00:26:08]: Yeah, we can put it down in the screen, and so ‘cause we, when we were talking.Swyx [00:26:11]: I'll put it up on the, on the screen.Ivan [00:26:12]: Yeah, right.Swyx [00:26:12]: People can look it up if they need.Ivan [00:26:14]: Look it up. And, yes, but they still have CPU and RAM, allocation that you have to have up and running. And so CPU and RAM, you have to allocate that and have that ready. And so there's basically two ways to do it. One is you either over-provision and you can handle the bursts, or two, you basically have, I don't know if this is a term, just-in-time compute, which is like as your load becomes, as your usage comes in, you can fire off requests for VMs or bare metals at other cloud providers and then get them up and running.Swyx [00:26:43]: This is if you go above 100%, right?Ivan [00:26:45]: Yeah, this is.Swyx [00:26:46]: Like your overflow.Ivan [00:26:46]: If your overflow, like spillage or whatever you do.Swyx [00:26:48]: You probably lose money on it, but it doesn't matter, right?Ivan [00:26:50]: It, not Well, you might, you might not That is a more cost-effective way to do it but it's a slower way to do it. Because basically what you have to do is you have to like queue your requests, spin up these just-in-time compute, get it all ready, provision it, and then get your workload there. And so if the time isn't important that much, that's fine, and you can do that. But if your customer, and especially for, let's say, the RL training runs, the reason why a lot of people come to us is because GPUs are more expensive than CPUs, right? So you want your GPU running at, what, 100% the entire time. And so when you're running runs on CPUs, when the when the CPU cycle is like down and spinning up the next one, you want that to be instantaneous so that your GPU doesn't go down, right? And if you then have to like go out and provision machines, you're essentially telling the GPU that it has to wait, and that's incurring our cost. So there's things that you have to try to solve for there.RL Workloads, Declarative Images, and Kubernetes ReplacementSwyx [00:27:43]: Yeah, let's talk about the different workload, right? You said that, what was it? A few months ago, you had zero RL workload and now it's 50%.Ivan [00:27:52]: It will be this one, 50%, yeah.Swyx [00:27:54]: Let's talk about how different it is, right? Like I imagine, for example, a lot less dynamic code generation of like arbitrary code. Like here, it's probably all the same code. You're just doing parallel runs or something, I don't know.Ivan [00:28:05]: Yeah. So you'll have multiple Depends on the like for each run, you'll have a snapshot. And they, for the most part, they actually do use our declarative image builder, which is like, “Oh, we, the agent wants these dependencies, these env vars.”Swyx [00:28:17]: These ones, yeah.Ivan [00:28:18]: Yeah, the declarative image builder, it.Swyx [00:28:20]: Which is a very modal like thing that they.Ivan [00:28:22]: Yeah. And so we build it on the fly and then we propagate that snapshot, and you can spin up as many sandboxes as you want against that snapshot. And then if you have to do changes, the model can, or like it could be also be automated. It's like, “Oh, now for the next run, we need to install these things or remove these things or whatever to get, a task done,” and then it goes off and runs that. So yes, that is something that it seems that they prefer. The number one reason I found, or should I say, let's take a step back. What we are competing against in that environment is essentially managed Kubernetes. So EKS, GKE, whatever. That is what the vast majority run on. And anyone that has tried Daytona versus GKE, EKS is like, “I'm never going back.” That has always been. There's a few reasons. One is the ergonomics. So if you have, if you're using Kubernetes to spin that up, you have to essentially manage the interface interactions with that. Daytona, although as a compute provider, it's more akin to a Twilio and Stripe from a consumption perspective than it is an AWS. Like you have an API, an SDK, it's quite like easy and seamless to get these things up and running, that's one. The other is the speed to which we spin up, which we mentioned earlier, which is much faster, and the scale to which we can go to. We haven't got into features, but an interesting feature is that it's very hard to OOM, or out of memory, our sandboxes, because we can dynamically on the fly.Swyx [00:29:48]: Resize.Ivan [00:29:49]: Resize, which is like impossible on almost any other thing. There are some technologies that enable you to do that, but it's like a very hard thing. And so we actually saw this when, the Terminal Revenge team is, brought us actually. So thank you, Alex and the team, that brought us into this whole space.Swyx [00:30:05]: It's just very rare that, a framework would just say, “Guys, just use Daytona.”Ivan [00:30:11]: Yeah, I think it says it somewhere. Yeah.Swyx [00:30:13]: Yeah. I was like, “What is this?”Ivan [00:30:15]: There's all, there's multiple there, but they also mention a few other places. and so Daytona specifically-We have, the, just jumping on themes here We, I don't know where it says Data Center.Swyx [00:30:27]: I, there.Ivan [00:30:27]: Doesn't matter.Swyx [00:30:28]: There's a very strong recommendation, which is, very unusual. Which is, it's.Ivan [00:30:33]: We do not pay them for this, just.Swyx [00:30:34]: I know, yeah. They just like you.Ivan [00:30:35]: Yeah, they like us. yeah, and also a thing, so, Data Center has multiple isolation sets underneath. The customer doesn't have to know what they are. But basically we have Docker, which is a container, that's hardened with Sysbox. So it's Docker's, isolation that is a security equivalent to a VM, but it's still a container. And that is the default, and they, especially in these training workloads, really like that as an interface to be able to use just a basic Docker container, and we enable Docker and Docker. Which for these RL runs, if you need to do a Docker compose or Kubernetes, you can spin up a K3S inside of these things, which unlocks a huge amount of workloads that you can do that you cannot do on other providers. So just on that part is much more interesting. And so we went that, through that. We showed them that we could do that, and they enjoyed that quite a bit. They being the general venture people.Swyx [00:31:28]: Those people, yeah.Ivan [00:31:29]: And Harbor people.Swyx [00:31:29]: Harbor people, do are they, are they a company yet?Ivan [00:31:33]: As far, I do not know.Customer Pull, Slack Connect, and the Computer Use BetSwyx [00:31:35]: Okay. All right. Yeah. It's like super obvious that like, there's a lot of excitement and success around these things, okay, so yeah, tell us more, right? Like, this is an exploding workload, Harbor adopted you, which helped speed things along. But what are you learning as this new workload comes online?Ivan [00:31:53]: There's a couple things that we learned, which we chat about in the beginning. We, and this has led our story, as we mentioned, we like talked to a lot of customers along the way, and we add more features and more tool sets as we talk to customers. And it's interesting that And I think it's that the ecosystem is so small and/or the models get smarter, where when we see one user come with a request, we know it goes on a roadmap if like three to five customers come with the same request in that week. It's like very bizarre. It happens so many times, which is.Swyx [00:32:27]: Because they're all friends.Ivan [00:32:28]: Sorry?Swyx [00:32:28]: They all, they're all friends. They're all in the same group chat.Ivan [00:32:30]: Yeah, probably, yeah. ‘Cause and they're like, “Oh, can you do this?” And I'm like, “Okay, this is interesting. We'll put it on a feature request.” And then the next one's like, “Oh, can you do this?” “Okay.” It's all the same, right? It's always the same. And so what we try to do, and I personally try to do, I try to be on as many call, quote-unquote “sales calls” I can. I'm in every Slack channel. We literally have about 1,000 Slack Connect channels, something like that. It's an interesting, there's so many interesting things you find out when you have all the Slack channels. You can also see where people, transfer between companies. You see leave Slack channel, enter Slack channel. It's an interesting thing. Also, just I digress, I feel that Slack Connect is literally LinkedIn what it should be. You have a list.Swyx [00:33:08]: LinkedIn charges you to, use your own connections, but Slack doesn't, right? Slack is like, do it for free. It's more lock-in. It's great.Ivan [00:33:15]: Yeah. It's amazing. Yeah. It's one of the reasons.Swyx [00:33:17]: You're gonna pay Slack for life.Ivan [00:33:18]: Exactly. You're there for life. So that's interesting. And so one of the things, the newer things we were talking about earlier is we made a big bet and put a lot of investment on computer use. that is not seen publicly the light of day. We haven't GA'd that yet, but we have.Swyx [00:33:32]: Is there a thing I can pull up?Ivan [00:33:33]: There is computer use there. It's right up a bit.Swyx [00:33:36]: Oh, yeah. Okay.Ivan [00:33:38]: What we have, what we talked about and what we've seen publicly is there's this theme now about, the human emulator where And Elon from XAI has talked about this publicly, and if you think about the models today, they're actually quite sophisticated and they can do a lot of work, but they still don't have access to all the tools. Like, I'm a strong believer that the most efficient way for an agent to work is essentially headless or through, terminal or whatnot. But if we, if we look at knowledge work in general, there's about 100 million knowledge workers in the US, about a billion in the world, and knowledge workers, and the salaries of them aggregate to 10 trillion in the US 50 trillion worldwide.Swyx [00:34:24]: Wow.Ivan [00:34:25]: Something like that. And if we look at, the five most important sectors of that, so like healthcare and government and financial services and whatnot, that's about 56% of that. So let's say it's about half of that. So in the US it's about 25 trillion, and most of them, most of that work is actually still locked into legacy apps inside of Windows, which is not going anywhere for a very long time. Like, people just won't invest in that. How much of it? our assumption is the following: if, in the RPA market, which is similar market, well, not the same 25% of, these white collar, workers', work is automated. If an agent is more sophisticated, can go through more runs, figure stuff out, let's say it's, 40%, right? And so if you take 40% of that, you get to essentially, $10 trillion a year.Swyx [00:35:17]: That's a TAM.Ivan [00:35:18]: That is a that is a TAM. So that's the TAM of the models, right? That's not our, essentially ours. But you get to that size, and to be able to do that, you essentially have to give agents these computers with the legacy. So computer use, either Mac or Windows or Linux. Linux we also obviously have and others have. But Windows specifically is something very new, and the only option right now is an EC2 with, Windows or on Azure. Both of them take anywhere from three to five minutes to spin up. We've created an actual sandbox, so it's a second instead of milliseconds, but you have, point in time snapshots, you have, forking, you have all the things that you have from a sandbox, but essentially enables you to hopefully unlock all this value. And so that's been our big push and bet, but we've sort of, kept our ear to the ground. What is sort of the next things in the market?RPA Returns: Why Agents Still Need ComputersSwyx [00:36:06]: Yeah, knowledge work, and building, and sort of RPA, the next wave of RPA. I got very excited about RPA kind of during COVID times. The UI path was IPO-ing. And it was, a very hot Isn't it, Eastern European?Ivan [00:36:20]: It is, Romanian.Swyx [00:36:21]: Romanian?Yeah, it might be the only Romanian, big unicorn okay, yeah. This I don't I don't, I don't have like a I think there's, I think there's a stage being set for the resurgence of RPA, ‘cause everyone understands that, yeah, no one wants to deal with these shitty apps and no one's gonna rewrite them. Like, you just have to do, a remote operation and programmatic operation of them.Ivan [00:36:45]: If you wanna unlock it, my own setup was basically the following. So I was doing a board deck recently, last month, whatever, and I'm like, “Okay, let's just, let's just do automated.” So, all our data's in, ClickHouse and PostHog and QuickBooks, where everyone else's is, and I'm basically, connected that all to, my Cloud code, like go off and go Cloud code whatever. Go off and, here's the integrations, go do that. It pulled out the first report, which was great. It connected to Brex and all these things, pulled it, which was great, and then I say, “Okay, now pull out this, and this,” and I kept getting, really well McKinsey-style design reports, but the data said partial data. all the missing data, partial data. Like, it can't access all the things, and I got so frustrated, and so I got, I got, my Mac Mini virtual sandbox with OpenClaw. I gave it its own account in our company, and then I went to all these services and created a read-only account, so literally like an intern in your company. And so I would say, “Now go and do this report,” and it would get the same, or like, “I can't via the MCP or the API or whatever. I can't get all the information.” I'm like, “Go log in.” And it will log into the website, then go in, export the data. It'll export the data and do the thing end to end. So even for things that have today APIs, not all of it is exposed, and I to get value, I get immense value right now, but it has to be a computer usage, unfortunately, and so I spend a bunch of tokens just on that, but I get the job done. And so if even a startup like ours, and using all the hottest tools, still needs a computer agent what hope does, Goldman have to have a headless, right?Swyx [00:38:22]: Yeah, what a - Why isn't Microsoft doing this?Ivan [00:38:27]: I'm pretty sure, Satya had a post yesterday.Swyx [00:38:29]: Oh, okay. I see.Ivan [00:38:29]: Which was like, “Every agent needs a computer.”Swyx [00:38:31]: I see, I see.Ivan [00:38:32]: So they have launched something recently.Swyx [00:38:34]: Yeah, they have Microsoft Power Automate, I'm sure, I'm sure, they're gonna have their version.macOS Sandboxes, Apple Constraints, and the Windows OpportunityIvan [00:38:39]: Version of that, yeah.Swyx [00:38:39]: You're gonna try to do yours, and it - I always know there's always demand for Mac, but I know it's, tricky to host, macOS sandboxes.Ivan [00:38:49]: We will have macOS sandboxes fairly soon. The problem with macOS, OS sandboxes is, I'm deep in this, I don't know how much interesting is.Swyx [00:38:55]: No, it's.Ivan [00:38:56]: MacOS has this problem.Swyx [00:38:57]: It's a licensing thing, right?Ivan [00:38:58]: Licensing thing. So one, you're allowed to run only two parallel VMs per machine, so that's one. Two, you can only license to a different user every 24 hours. So if you come in and theoretically, if I wanna charge you per second and I charge you one second, I have to have it idle for the rest of the day. I can't have anyone else doing that. So the pricing will be different in the sense that I will have to - we would have to charge for 24 hours, and that's not even, that's not even the most difficult thing. But the, thing above that is, from a security perspective, they enable you to do memory snapshot, pause, resume, but only on the same physical drive, physical machine. And so what you can do in, Windows world or Linux world is that I can move in the background, your snapshot from one to the other and manage load, right? Here, if you wanna do that, you essentially have to have your.Swyx [00:39:49]: Yeah, snapshots. Yeah.Ivan [00:39:50]: Your.Swyx [00:39:51]: It's like.Ivan [00:39:51]: Physical machine.Swyx [00:39:52]: You can't break it up.Ivan [00:39:53]: You can't, you can't move things around that, and all of that is, that part is, from a security standpoint, if it is written. Like, I understand the security aspect of that, but it disables you from doing these agentic, like really scalable agentic workloads.Swyx [00:40:08]: You need to do a vibe-coded, clean room implementation on macOS that you can then - That's like Clean OS or something. I don't know.Ivan [00:40:17]: So. We have.Swyx [00:40:18]: ‘cause like Linux was originally like a clean room rewrite of Unix.Ivan [00:40:21]: Okay. Yeah.Swyx [00:40:21]: Or something like that, right? Like same thing to macOS. Someone needs to do it.Ivan [00:40:25]: Someone will do that, and someone will have some long-running agents for a few days to figure this stuff out. But yeah. So definitely we - we're really close to offering something ‘cause people do want it, but the pricing will be different, and the feature set will be sort of stringent.Swyx [00:40:38]: Yeah, nobody's gonna use this. like, the labs, the labs will because they want to automate macOS.Ivan [00:40:42]: They have to do RL. They have to do RL again. But even if you The - So the point is with the RL part, if you, if you do RL on macOS, then the next iteration of the model comes out, it will be able to use these tools significantly. Then you actually need to run those, that somewhere. So you're gonna have to have that, later on. And from, if anyone at Apple is listening, I very much feel that they are shooting themselves in the foot of the scale of the revenue of compute or licensing they could get if they would just enable a concurrency model similar to what you can get on a Windows and a, and Linux.Swyx [00:41:17]: Yeah. Yeah. And I'm sure they've heard this before. They just don't care. Yeah, it's And maybe they will change their mind with the new CEO.Ivan [00:41:24]: Yeah. We'll see.Swyx [00:41:25]: We'll see.Ivan [00:41:25]: High hopes.Swyx [00:41:26]: High hopes.Ivan [00:41:26]: High hopes.Swyx [00:41:27]: Okay. But I, it's very clear the market opportunity is huge in Windows, and you can go for a long time on just Windows, but your customers are gonna want both. and I think, it is interesting to me that, this is the sort of God application of agents, right? Like, I don't It was - How big was OpenClaw for you guys? Like, was it, was there, a significant bump.OpenClaw, Agent Labs, and the B2B2C Sandbox MarketIvan [00:41:54]: Not for us because we.Swyx [00:41:54]: Because you already.Ivan [00:41:55]: We're kind of positioned differently. Whereas although it's completely PLG and we have individual developers that use it, most of the users that use Daytona are sort of a B2B2C. Sort of it's either B2B or B2B2C. So, in the researcher world, it's B2B, so you're selling to, labs and neo labs and things like that. But on the long-running agents, it's mostly, from a scale revenue perspective, it's mostly B2B2C, where you have a app layer agent that uses you at a big scale.Swyx [00:42:26]: Like a Manus. Yeah.Ivan [00:42:28]: Like a Manus Lovable type of thing.Swyx [00:42:31]: Yeah. I think that's the question of, well how, um-Uh, yeah, B2B to C is basically to me what I've been calling an agent lab, which is kind of like you're not in a model lab, but you're making a very good wrapper that is a platform that other people can sign up so they don't have to code those things. Yeah, it sound, it sounds like a much better market than the direct OpenClaw market.Ivan [00:42:56]: I've like - We I've done multiple things. So the CodeAnywhere's part of our career path R in the calendar, was very much an end user developer product. And so that is great. It You can get a lot of developer love, and I feel that we do as a company have a bunch of developer love. But it's a different type, where it's people building these things. Again, it's more akin to a Twilio because you don't really run - As a person, you wouldn't run Twilio. I don't know how many people remember. It was like ask your developer billboard and whatnot. And people really love Twilio, but they only used it inside of like, “Oh, I'm building this app or service for thing.” And so we're very much directly to that. And you also know that I used to work for a competitor for Twilio, so it's kind of ingrained, in my DNA.Swyx [00:43:35]: People don't know InfoBip is that big.Ivan [00:43:38]: Yeah, it's.Swyx [00:43:39]: Because.Ivan [00:43:40]: It's a billion euro.Swyx [00:43:40]: They're all American. They're like, “Whatever's in Europe doesn't matter to me.” But like it's the, it's the same size or bigger? Same size?Ivan [00:43:46]: It's about half the size.Swyx [00:43:47]: Half the size?Ivan [00:43:48]: Yeah, about half the size.Swyx [00:43:48]: It's like, yeah.Ivan [00:43:48]: Still huge. Multiple billions a year. Yes.Swyx [00:43:51]: That's crazy.Ivan [00:43:51]: Exactly, and so that - These are like really interesting and large revenue-generating, very sticky businesses. Whereas when you're selling to the - When your focus is the end developer, it is a very hard sell because they're very price sensitive, very price conscious, very around that. And there's very It's very hard to scale. Your cap is the number of people that are willing to spin up - First of all, wanna spin that up, and then spin up multiple of these. Whereas if you're in the enterprise one, like we know everyone's talking about like how many tokens they're spending, I'm spending. Like a lot of companies today are like, “If this is our company, spend as much as you can.” Like basically that is where we're going. And so if you think about that paradigm, where you're selling to companies that say, “Spend as much as you can to generate, productivity,” versus, “Oh, I'm a single person. I have this much budget, and I'm doing this thing because it's fun or it's helping me out or whatever.” Like it is a different, it's a different go-to-market, I think, strategy.MCP, CLIs, and Sandboxes as the Agent RuntimeSwyx [00:44:50]: Yeah, there's a lot of discussion. I'm just kind of going through like the mental list of things that are in your favor, which is, for example, MCP versus CLI. Like obviously you want CLI. It's been very good for you. I feel like it's maybe a drop in the bucket or maybe it's huge. I'm just checking whether it's like these are big trends.Ivan [00:45:10]: Those things you - work well in our favor, to your point just because every.Swyx [00:45:13]: They're kind of drop in the bucket, right?Ivan [00:45:15]: I think it's like sort of all the things come together. And so there's so many things that impact that. To your point, like OpenClaw wasn't huge for us, but like having the agent SDK, from Anthropic, so or Cloud Claude Code was very interesting. The reason why it was interesting is that a lot of, let's call them app I don't know what to call them, app layer agent companies, essentially they are like, “Oh, I can create this new app, this new agent. All I need, I just use Claude Code, and I throw it into a sandbox, and then I have my interface to the human to that.” And so that enabled so many more companies to actually offer this, and then they would pull on sandbox. So that was, that was interesting. And to your point, like MCP, versus the CLI, the MCP is an interface against an API, whereas the CLI is like you can actually go do things. Like this is it. The difference between integrations and actually running scripts or data or analysis against a thing. So being able to use a CLI very well enables the agent to do more things, and it's because that people will invoke a sandbox, they'll run it in the CLI, and but it'll do anal-analysis on that data and then give you an actual result versus just, pulling data from an API source.Swyx [00:46:29]: Yeah, it's a layer of indirection basically, it's the same thing as agentic search versus RAG, which where you're.Ivan [00:46:34]: Exactly, yeah.Swyx [00:46:34]: Just like you just win whenever people put more agents into their workflow. And so like it doesn't really matter, but I'm just kinda teasing out like what else have people heard about that like it's sort of, “Oh yeah, this is another sandbox use case. Oh yeah, that's another one.” Am I, am I missing any big ones?Ivan [00:46:51]: The thing, the thing that people, which is the computer use stuff, which I think is probably the most interesting one, is, and to your point, we've talked to so many people over the last year. It's like, “Oh, like why do you need a sandbox? Why do you need this? Why this?” And to your point, it's like, “Oh, I need sandbox for this. I need sandbox for that. I need sandbox-” It's like, “Oh, I need it for every single thing.” And so basically what I, what I - and it sounds like a broken record, it's like you use a laptop every single day, right? And you are n of one. It's just you. But now imagine how And by the way, the laptop, the computer PC market, the PC market is about equal to the cloud market in total. So it's about 150, 180 billion a year. Something like that. It's about roughly the three cloud hyperscalers is about equal to like Apple, HP, Lenovo, whatever, It's a little bit less, but it's sort of like that. And now imagine And that's just like, so how big is the addressable market? What, how many people are there in the world now? What's the last data?Swyx [00:47:45]: Let's call it eight billion.Ivan [00:47:46]: Eight billion. And so let's say you can have two computer, like you have one personal and one business, whatever. Like so it's double that, right? and so that's 16 billion, right? How many agents are gonna be running in two years, in 10 years, in 100 years? Like And for every single task, they will need one of these. And so how big is that? That market is essentially quote unquote “infinite”. You will get to the point, and Dylan Patel was at the conference talking about, from SemiAnalysis, that talks usually about GPUs, was also talking about how CPUs will now be a bottleneck because it will be the constraint. You won't be able to grow, or we won't be able to have enough of these because there won't be enough CPUs to basically do.Swyx [00:48:23]: Yeah. Well, I actually had a really good podcast with Doug Oliphant, who, which was his president at SemiAnalysis, where they've basically been like, yeah, it's been a GPU shortage first, but then it's cascaded down to memory and now to CPUs.Ivan [00:48:35]: CPU, yeah.Swyx [00:48:35]: It-What's next? So networking. So, networking actually has been in shortage for a while if you're looking at, just GPU networking. But, yeah, it's really crazy the amount of computer use that's going on, yeah, cool. I, other questions are, just the one very big part is the open sourceness which you didn't have to do, your competitors don't do, like it's not, a lot of people are worried about keeping their projects open source because some competitor can just slot fork it. I don't know if there's any reflections on just being an open source company.Open Source, Trust, and Enterprise ProcurementIvan [00:49:15]: Yeah. There's a bunch. So we the original product that we did was open source.Swyx [00:49:19]: Yeah. CodeAnywhere.Ivan [00:49:20]: So doing that was actually very good for us. There's basically a saying of, What's the saying? Like, companies that are, that are doing really well, measure themselves against, free cashflow, that are kinda okay, it's EBITDA, then, it's, it goes all the way down.Swyx [00:49:36]: The worst is like GitHub stars.Ivan [00:49:37]: GitHub stars. GitHub stars are the worst, yeah. So you go all the way down to GitHub stars. And so our original one was GitHub stars. That's what we talked about, we're at the point we're talking about revenue, so we're we've gone up the stack on that. And so we started.Swyx [00:49:47]: No, profit.Ivan [00:49:48]: Yeah. We haven't, we're, we'll get there. We'll get there. But basically at that point we did stars and GitHub and it was useful, and the original variation that we did, it we split the core into its own repo and it was Apache 2.0, so very, permissive. And then we basically would bundl

This show has been flagged as Clean by the host. Hi this is your host, Archer72 for Hacker Public Radio. In this episode I share some of my findings about a problem with the Newsboat naming of the HPR feeds, which was brought up in comments about my Newsboat show, HPR4424. hpr4424: How I use Newsboat for Podcasts: comment #6 : download-filename-format for HPR podcasts Ken already had some findings of his own about the ccdn.php extension in the feed. hpr4424: comment #10 : Summary of findings I thought that this might be able to be fixed on an invididual basis, and set out to ask Claude.ai a few questions. But first, some colaboration from Dave Morriss about a good renaming format. This was definitely more on Dave's side than mine, but came up with this. You can tell Dave's handywork from the short variable names, which stems from his extensive experience on Unix type machines in the University days. exif-rename-hpr-dave.sh #!/bin/bash URL="$(cat /tmp/hpr-url.txt)" echo "DEBUG URL: $URL" >> /tmp/hpr-debug.log AUDIO_URL="$(curl -s "$URL" | grep -Eo 'https?://[^"]*.(ogg|mp3)' | head -1)" echo "DEBUG AUDIO: $AUDIO_URL" >> /tmp/hpr-debug.log if [[ -z "$AUDIO_URL" ]]; then echo "ERROR: Could not find audio URL from: $URL" >> /tmp/hpr-debug.log exit 1 fi # Changed destination to HPR-queue DEST=~/podcasts/hub.hackerpublicradio.org/HPR-queue/ # Record files present before download BEFORE="$(ls "$DEST"*.{ogg,mp3} 2>/dev/null | sort)" wget -nc --content-disposition -P "$DEST" "$AUDIO_URL" cd "$DEST" # Record filename just downloaded (new file not in BEFORE) AFTER="$(ls "$DEST"*.{ogg,mp3} 2>/dev/null | sort)" DOWNLOADED="$(comm -13 /dev/null | sort)" RENAMED="$(comm -13 /tmp/hpr-url.txt && ~/bin/download-and-rename-hpr.sh"; open-in-browser ; set browser "your-normal-browser" A few Claude questions later… After asking to add a function to the macro in order to add the resulting downloaded file to the queue, the file name hpr1234.ogg was being added to the queue instead of the renamed file. Two things to fix: The renamed file isn't being found because ls -t runs before the rename has fully settled, or the glob isn't matching the new filename format (which includes spaces and semicolons) The existing queue entries show the correct format: "url" "path" downloaded — we need to match that, with downloaded status and the full renamed path including extension Exif rename script #!/bin/bash # ~/bin/download-and-rename-hpr.sh URL="$(cat /tmp/hpr-url.txt)" echo "DEBUG URL: $URL" >> /tmp/hpr-debug.log AUDIO_URL="$(curl -s "$URL" | grep -Eo 'https?://[^"]*.(ogg|mp3)' | head -1)" echo "DEBUG AUDIO: $AUDIO_URL" >> /tmp/hpr-debug.log if [[ -z "$AUDIO_URL" ]]; then echo "ERROR: Could not find audio URL from: $URL" >> /tmp/hpr-debug.log exit 1 fi DEST=~/podcasts/hub.hackerpublicradio.org/HPR-newsboat-test/ # Record files present before download BEFORE="$(ls "$DEST"*.{ogg,mp3} 2>/dev/null | sort)" wget -nc --content-disposition -P "$DEST" "$AUDIO_URL" cd "$DEST" # Record filename just downloaded (new file not in BEFORE) AFTER="$(ls "$DEST"*.{ogg,mp3} 2>/dev/null | sort)" DOWNLOADED="$(comm -13 /dev/null | sort)" RENAMED="$(comm -13 /tmp/hpr-url.txt && ~/bin/download-and-rename-hpr.sh"; open-in-browser ; set browser lynx Provide feedback on this episode.

Jim talks with Peter Wang—chief AI officer, cofounder and CEO of Anaconda, board member of the Center for Humane Technology, and founder of the Austin STEM Center—about Robert Pirsig's metaphysics of quality, how modernity encourages defection, and a secular conception of the sacred. They discuss: Peter's self-description as "the music in a violin that can kind of hear itself" The "Peter Wang-shaped hole in the universe" thought experiment Subject-object Cartesian dualism as a false alienation Minimum viable metaphysics & atheistic agnosticism Religion as an evolutionary emergent coherence mechanism for human collectives Figure and ground as a metaphysical lens—the anonymous soil that allows religion to sprout The Unix fortune "Man was invented by water to carry itself uphill" & Peter's teleology origin story Process metaphysics & presentism—"we're not going anywhere, we're becoming someone" Pirsig's metaphysics of quality & the four strata of static patterns of value The intellectual plane vs. the social plane & Ken Wilber's pre-trans fallacy Defection within collaborative groups as the dynamic all human social systems try to constrain "Death from a Distance"—throwing, beta coalitions & the emergence of a middle class of power Modernity's shrinking locus of care & the collapse of embedded social context The agglomeration of defectors & how fluid capital enables sociopathic hoarding Money-on-money return as today's dominant pruning rule Joint attention as a scarce collective resource & social media's perforation of shared intersubjective infrastructure Human agency & "micro-abdications" as the aggregate source of Moloch / Game A The augmented currency thought experiment—metering human thriving alongside financial returns Broken collective sense-making & the search for dynamic, adaptable values Peter's secular conception of the sacred—the "eternal golden braid of humanity" "Ofness"—holding both distinctness and belonging to the world ... and much more. Links: Episode Transcript JRS EP 278 Peter Wang on AI, Copyright, and the Future of Intelligence JRS Currents 092: Peter Wang on The Meaning Crisis and Consequentiality JRS EP 16 Anaconda CTO Peter Wang on The Distributed Internet "The Silent Sky and the Test Ahead," by Jim Rutt "A Minimum Viable Metaphysics," by Jim Rutt Zen and the Art of Motorcycle Maintenance, by Robert M. Pirsig Lila: An Inquiry into Morals, by Robert M. Pirsig Chaos: Making a New Science, by James Gleick Death from a Distance and the Birth of a Humane Universe, by Paul M. Bingham and Joanne Souza The Selfish Gene, by Richard Dawkins Center for Humane Technology Peter Wang is the Chief AI and Innovation Officer and Co-founder of Anaconda. Peter leads Anaconda's AI Incubator, which focuses on advancing core Python technologies and developing new frontiers in open-source AI and machine learning, especially in the areas of edge computing, data privacy, and decentralized computing.

Switching from Proxmox to Sylve, FreeBSD Quarterly report, FreeBSD's laptop program, Migrating ZFS, Haiku and OpenSSL news, and more... NOTES This episode of BSDNow is brought to you by Tarsnap and the BSDNow Patreon Headlines I Switched from Proxmox to Its FreeBSD Counterpart on My Home Server – Here is How it Went FreeBSD Quarterly Report The FreeBSD Foundation's Laptop Support Project News Roundup Migrating ZFS filesystems from one zpool to another – same host Haiku Isn't Just For X86 Anymore, Boots On ARM In QEMU OpneSSL 4.0 Other schedulers? Illumos? Tarsnap This weeks episode of BSDNow was sponsored by our friends at Tarsnap, the only secure online backup you can trust your data to. Even paranoids need backups. Feedback/Questions Send questions, comments, show ideas/topics, or stories you want mentioned on the show to feedback@bsdnow.tv Join us and other BSD Fans in our BSD Now Telegram channel

This show has been flagged as Clean by the host. This series is dedicated to exploring little-known—and occasionally useful—trinkets lurking in the dusty corners of UNIX-like operating systems. I would imagine that most users of UNIX-like systems have heard of cron —certainly any system administrator should have. Briefly, cron is a way of running a job repeatedly based on the time and date; for example, a job could run every hour, at 5:00am every Tuesday, or the 3rd of every month. It is commonly used for administrative or maintenance tasks that should be done on a regular schedule, such as checking for software updates, rotating log files, or updating the database for the locate command. As well-known as cron is, there is a similar utility that very few seem to be aware of: at . This is the word "at", and has nothing to do with the at symbol "@". An at job is very much like a cron job, except that an at job only runs one time. A job is submitted by running at timespec 1 , where timespec is the time and date the job is to be run. The linked POSIX specification page describes acceptable formats for timespec ; some examples are " now ", " 14:00 ", " noon tomorrow ", " 14:00 + 3 months ", and " 14:00 January 19, 2038 ". The utility then waits on standard input for you to enter a set of commands to be run in the job. You end input by typing Control-D to mark the end of text. (As an alternative to typing in the job, you could instead use the "

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Tyler Cloutier, founder of Clockwork Labs and creator of SpaceTimeDB. They explore how SpaceTimeDB functions as more than just a database—it's essentially a distributed operating system that merges server logic with data storage, enabling real-time applications and time-travel capabilities. The conversation ranges from the technical architecture of databases and operating systems to the philosophy of distributed systems, touching on everything from Unix and Linux to how SpaceTimeDB could revolutionize AI-generated software deployment. Tyler explains how their system reduces the complexity of building real-time applications, makes deployment simpler for both humans and AI agents, and why games like their MMORPG BitCraft Online drove them to create this new infrastructure. They also discuss the future of the internet, the role of bots in gaming, and how SpaceTimeDB fits into the broader landscape of cloud computing alongside tools like Cloudflare, Vercel, and Docker. For more information, visit spacetimedb.com or check out Clockwork Labs on GitHub and Twitter.Timestamps00:00 Stewart introduces Tyler Cloutier, founder of Clockwork Labs, discussing the origin of SpaceTimeDB's name inspired by Einstein's theory and its time travel capabilities that store all operations indefinitely05:00 Tyler explains SpaceTimeDB as more of an operating system than a database, using tables instead of file systems while running code in a sandboxed environment with full atomic properties10:00 Discussion of how SpaceTimeDB replaces both Node.js and Postgres by merging web server and database functionality, eliminating separate deployment concerns15:00 Tyler explains JavaScript execution through Chrome's V8 engine and JIT compiling, leading to Node.js creation for server-side JavaScript development20:00 Explanation of stateless web servers versus stateful game servers, and why games require in-memory state management for real-time performance25:00 Tyler introduces reducers and real-time subscriptions, questioning why more applications aren't real-time when state changes should update immediately30:00 Discussion of Facebook as essentially a text-based MMO, comparing social media architecture to game server requirements and the need for unified systems35:00 Tyler explains ACID properties in databases: atomic, consistent, isolated, and durable, using game item trading examples40:00 Comparing SpaceTimeDB to smart contract systems without cryptocurrency or global consensus, positioning it as a smart database with centralized trust45:00 Tyler reveals SpaceTimeDB uses 43% fewer tokens than Postgres for AI-generated applications, making it valuable for vibe coding platforms50:00 Conversation shifts to bots in games and proof-of-human concepts, with Tyler proposing biometric systems and discussing potential in-person gaming applications55:00 Closing discussion about tracking AI-driven traffic through UTM parameters and finding SpaceTimeDB at spacetimedb.comKey Insights1. SpaceTimeDB is fundamentally a database that runs application code directly inside it, combining what traditionally required separate systems like Postgres and Node.js. Users compile their application logic into WebAssembly or JavaScript and upload it to run within the database itself. This architecture provides high performance because the entire server backend operates inside the database environment. The system also features time travel capabilities, storing every operation and change to data persistently and indefinitely, allowing users to set application state back to any earlier point in time. This makes SpaceTimeDB more accurately described as an operating system rather than just a database, where the abstraction is that everything is a table rather than a file.2. The inspiration for SpaceTimeDB came from building BitCraft Online, an MMORPG where all players exist in a single persistent world and rebuild civilization together. Traditional MMO backends required complex custom solutions to handle real-time state, with game servers storing state in memory and periodically writing to databases. This complexity existed because games cannot afford the latency of constantly delegating to distant databases like traditional web applications can. SpaceTimeDB solved this by making the database fast enough to handle real-time requirements directly, eliminating the need for separate game servers. This same performance advantage that benefits games also applies to web applications, which is why SpaceTimeDB evolved from a game-specific tool to a general-purpose platform.3. SpaceTimeDB functions as a distributed operating system where each database acts like a process in an actor model system, similar to Erlang or Scala Akka. Databases can send messages to other databases and be spawned across a cluster for horizontal scaling. This represents an overlay operating system running on top of Linux rather than competing with it, providing a distributed abstraction across many machines while Linux handles device drivers and hardware support. The vision is for the cloud to function as a single enormous computer running one operating system, where developers simply publish their programs without managing separate services, deployment, routing, networking, or persistence infrastructure.4. The real-time capabilities of SpaceTimeDB address a fundamental limitation in how most web applications work today. Traditional web servers are stateless, delegating all state to databases and accepting network round-trip latency for each request, which is why users often must refresh pages to see updates. SpaceTimeDB allows queries to be subscribed to, maintaining open connections that stream changes whenever query results update. This makes applications like Discord, Facebook, or banking systems naturally real-time without requiring page refreshes. The historical accident that more things are not real-time represents a problem SpaceTimeDB solves by unifying the web world with the game world's real-time requirements.5. SpaceTimeDB implements ACID properties—Atomic, Consistent, Isolated, and Durable—ensuring database operations are reliable and safe. Atomic means operations either fully happen or not at all, preventing issues like item duplication in games when trading between players. Consistent means declared invariants like unique usernames are always enforced. Isolated means concurrent operations do not interfere with each other. Durable means changes persist even if computers restart, with varying levels from in-memory on one machine to disk storage across multiple geographic locations. These properties are managed through reducers, functions inspired by React Redux that fold changes into application state incrementally.6. For AI and large language models, SpaceTimeDB offers significant advantages in building and deploying applications. Testing showed that creating applications with SpaceTimeDB uses 43% fewer tokens compared to Postgres implementations, costs less, has fewer bugs, and is easier to extend. This matters because the primary cost for vibe coding platforms is tokens. As more software gets written in the next twelve months than ever before, there is insufficient focus on infrastructure required to run all this AI-generated software. SpaceTimeDB positions itself as ideal for LLMs to target because of its simplified deployment model where developers just publish code and the system handles everything behind the scenes.7. SpaceTimeDB can be understood as a smart contract system without cryptocurrency or global decentralized consensus. Like blockchain smart contracts, it executes code with atomic, consistent, isolated, and durable properties, but avoids the expense and slowness of requiring all computers worldwide to agree on everything. Instead, it offers centralized trust where users trust Clockwork Labs not to modify deployed contracts, rather than the trustless but extremely costly blockchain approach. This makes it functionally similar to Cloudflare's durable objects but with full relational database capabilities. The system exists before the networking layer where Cloudflare operates, handling deployment, server, and database functions while Cloudflare could provide DDoS protection in front of it.

This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubIan F. Darwin - Java, Android & Unix Developer, Trainer, Mentor & Author of "Java Cookbook"Jeanne Boyarsky - Oracle Java Champion, Co-Author of "Real-World Java" & "OCP 21 Java Cert Book"Check out more here:https://gotopia.tech/episodes/438RESOURCESIanhttps://fosstodon.org/@IanDarwinhttps://x.com/Ian_Darwinhttps://github.com/IanDarwinhttps://www.linkedin.com/in/idarwinhttps://www.darwinsys.comJeannehttps://bsky.app/profile/jeanneboyarsky.bsky.socialhttps://mastodon.social/@jeanneboyarskyhttps://x.com/jeanneboyarskyhttps://github.com/boyarskyhttps://www.linkedin.com/in/jeanne-boyarskyhttps://sites.google.com/view/jeanneboyarskyhttps://www.selikoff.netLinkshttps://javacookbook.orghttps://dev.java/community/jcsDESCRIPTIONIn this GOTO Book Club, Java Champion Jeanne Boyarsky interviews Ian F. Darwin — author of one of Java's most enduring reference books, Java Cookbook, now in its fifth edition covering up to Java 25. The conversation traces Ian's extraordinary journey: from writing Java's first commercial training course outside of Sun Microsystems, to meeting Tim O'Reilly at a Unix conference and handing him a chapter on lint, to delivering a class in Houston where the entire room had just been laid off and were using the course as their golden handshake into a new career. Ian talks about the philosophy behind the book — culling a peak 900-page beast down to a tight 600 pages, anchoring tool choices on proven, battle-tested picks like JUnit, Mockito, and logging — and shares his three favourite chapters: Regular Expressions, Object-Oriented Techniques, and Reflection.The conversation gets sharply honest about AI and the future of the industry. Ian — who uses Claude as his coding assistant and does vibe code — warns that his greatest fear isn't AI taking over the world, but something subtler and more dangerous: companies stopping junior hires because AI can do the work, leaving no one to grow into the deep expertise that retires with the current generation. The parallel risk for books is equally candid: AI was trained on older editions, so the fifth edition is genuinely new and un-scraped territory.His advice for anyone who has learned the basics of Java?Don't ask an AI — buy the cookbook, save yourself years of trial and error, and for goodness' sake, read the code before you deploy it. "It's like building an airplane and putting passengers on it without flight testing."RECOMMENDED BOOKSIan F. Darwin • Java Cookbook 5th ed. • https://amzn.to/3QH0NZyIan F. Darwin • Java Cookbook 1st ed. • https://amzn.to/4sUpPlLIan F. Darwin • Checking C Programs with Lint • https://amzn.to/3Q2C69YVictor Grazi & Jeanne Boyarsky • Real-World Java • https://amzn.to/4oCEeBRJeanne Boyarsky & Scott Selikoff • OCP 21 Java Cert Book • https://amzn.to/4lF8OICBlueskyInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

Cybersecurity Looks Like Proof of Work Now, Compensating for RAM Constraints with L2ARC on ZFS, GhostBSD 26.1, and more... NOTES This episode of BSDNow is brought to you by Tarsnap and the BSDNow Patreon Headlines Cybersecurity Looks Like Proof of Work Now Compensating for RAM Constraints with L2ARC on ZFS GhostBSD 26.1 News Roundup I connected a phone to my FreeBSD server My Journey to the BSDs The unseen hero of OpenBSD Beastie Bits BSD Can Schedule up OpenBSD Campaign 2025 OpenBSD Campaign 2026 Tarsnap This weeks episode of BSDNow was sponsored by our friends at Tarsnap, the only secure online backup you can trust your data to. Even paranoids need backups. Feedback/Questions Send questions, comments, show ideas/topics, or stories you want mentioned on the show to feedback@bsdnow.tv Join us and other BSD Fans in our BSD Now Telegram channel

Breaking up Big Tech, Porting MacOS to the Nintendo Wii, OpenBSD on the Pomera DM250, Postgres is your friend and more... NOTES This episode of BSDNow is brought to you by Tarsnap and the BSDNow Patreon Headlines Breaking up with Big Tech Porting MacOS to the Nintendo Wii News Roundup Installing OpenBSD on the Pomera DM250 Postgres is Your Friend. ORM is Not Java Sun SPOTs I like to use Soviet control panels as a starting point Beastie Bits OSHintosh - an open source 68000 Macintosh Time to update 2.11BSD: biggest patch ever landed before 35th anniversary A quick and easy Guide to Tmux Tarsnap This weeks episode of BSDNow was sponsored by our friends at Tarsnap, the only secure online backup you can trust your data to. Even paranoids need backups. Feedback/Questions Producer Note, If you have emailed in and you havent heard back and we havent covered your message, email again. Our email is flooded with spam and I might have missed your message. Send questions, comments, show ideas/topics, or stories you want mentioned on the show to feedback@bsdnow.tv Join us and other BSD Fans in our BSD Now Telegram channel

While working with a customer recently, I heard this sentence: a tool is better than a script. The reference was that this customer preferred a known, tested, approved tool for most of their staff rather than a script built, lightly tested, and perhaps changeable by anyone in their organization. I was surprised, because in many ways, I've depended way more on scripts, more often, than "tools" in my career. Often I struggled to find tools that actually worked in the way I wanted them to and built them myself with Unix shell utilities, VB Script, PowerShell, or some combination of those or other technologies. Read the rest of A Tool is Better than a Script

This show has been flagged as Clean by the host. This series is dedicated to exploring little-known—and occasionally useful—trinkets lurking in the dusty corners of UNIX-like operating systems. Let me start by admitting that I've never actually seen the film referenced in the episode title, but I couldn't resist using it anyway. If you've used the UNIX command line to any extent, chances are good that you are familiar with the kill command. A common use is to terminate a misbehaving program. But there is more behind how kill works, including a curio you might not know about. The kill utility works by sending a "signal" to the targeted process. This signal is selected from a pre-defined list, and triggers the process to interrupt its normal flow and handle the signal before potentially returning back to its work. This "signal handler" can do whatever activities are written in its code, but typically it will take actions connected to the purpose of the specific signal received. One option is for the process not to have a signal handler at all; in that case, there is a default action that the operating system will take on behalf of the process, depending on what the signal is. The possible default actions are to terminate the process, take some implementation-defined action (usually writing a core file to disk) and then terminate the process, stop (pause) execution of the process, continue execution of a stopped process, or ignore the signal. By default, kill sends the TERM signal to the process, an indicator that it should terminate. Each signal has a name and a number assigned to it; SIGTERM is the name of the "terminate" signal. You can use the -s option with the name to choose which signal to send. The 'kill' command is specified to take these names without the SIG prefix, though some implementations will accept them either way. Also, kill is supposed to be case-insensitive when it comes to these names, but the convention is to write them in all upper case. The assigned numbers for signals can vary depending on the operating system, and on Linux, depending on what processor architecture you're on. However, there is a short list of signals 1 that have a stable number assigned to them. Despite this, I recommend using the signal name in your scripts to make them clearer and to ensure maximum portability to different systems. Well-behaved programs will have a signal handler that responds to the TERM signal by stopping what they are doing, cleaning up any open resources like temporary files, and promptly exiting. However, not every program behaves well, so sometimes it becomes necessary to send them the KILL signal. This one is special and cannot be handled or ignored by the program 2 ; the operating system will immediately terminate the program, possibly leaving a mess behind. Two other signals that can come in handy sometimes are STOP and CONT. As you might expect, STOP forces a process to pause in the middle of whatever it was doing. Its counterpart, CONT (short for "continue"), causes it to resume execution. This can be useful if a program consumes CPU time when not actually doing anything worthwhile—sending it the STOP signal will end that, and when you're ready to use it again, CONT will cause it to pick up right where it left off. Like the KILL signal, STOP cannot be handled or ignored by the program. I have used this to pause the game FreeCiv when I wanted to break away to do something else, but didn't want to have to deal with exiting my current game and having to reload it later. Take note, though, that the program might get confused if it expects the system clock not to suddenly jump forward, as that is exactly how the situation will appear to it. Network connections or other resources the process is using that change while it is stopped are other potential trouble spots. Also be aware that a stopped graphical program will not update its window, so I find it best to minimize the window before stopping it and then continuing the process before trying to raise the window again. Programs are not necessarily required to interpret signals in the way they are described. For example, the HUP signal was originally intended to be sent when a modem or serial connection hang-up occurred. Today, some daemons use it for other purposes and take a specific action in response. For example, the Apache web server will restart 3 , and NetworkManager will reload its configuration 4 . These uses of signals are usually described in the daemon's manual page, often in a separate section dedicated to signals. While all this background might be interesting (or maybe not), it's pretty commonly known, so isn't really a curio. Our UNIX Curio for today is the "0" signal. This is actually not a signal at all; instead, it tells the kill utility to just check for the existence of a process. If the process exists, kill will exit with a status of 0. If it doesn't exist, the exit status will be greater than 0. This provides a handy way to check whether a particular process is still around. A shell command can use this exit status with its control structures like if to take a particular action depending on whether a particular process exists. Somewhat oddly, "0" is both the number and the name of this pseudo-signal. Why would you want to do this? I have used it for a script to analyze log files that runs daily on a web server. Depending on how much traffic the site is getting, the log files can grow to the point where it takes longer than a day for the script to get through them. If a second instance of the script is started while one is still running, it will slow down both and if more keep being added, eventually the machine will run out of memory. My solution was to create a .pid file containing the process ID number of the running script. You might see examples of these if you look in /run or /var/run on your system. The script creates a file named something like "myscript.pid" in this directory containing its own process ID, which can be accessed in the shell with the variable $$ . When my script starts, it checks to see whether this file exists. If so, it uses kill -s 0 $(cat /run/myscript.pid) to see if the previous process still exists. If the process is no longer around, that's a sign that it exited abnormally before it had the chance to delete the .pid file, so the script removes the abandoned .pid file, replaces it with a new one containing the current process ID, and continues with its work. If the previous process is still around, my script exits with a message to that effect. This way, I can be sure that only one instance of the script will ever be running at one time. Be aware that the kill utility might also return a non-zero exit status if the user running it does not have privileges to send a signal to the process with the specified ID. This is not a concern if you are running a script as the root user, but could be if you are not. This can occur even if you aren't actually sending a signal, just using the "0" pseudo-signal to check if a process exists. There is a weakness in this method. UNIX-like systems generally have a limit to the quantity of process ID numbers that can be issued, so they are reused over time. (However, there will never be two processes with the same ID number running at the same time.) Typically, the first process that is run on start-up will be given the ID number 1, and each subsequent process will get the next higher number. Once the maximum is reached, the system starts again at the beginning with the lowest number not in use. It is possible for the script to crash and leave behind the .pid file, then the same process ID could be recycled and actively used for another program, causing a new instance of the script to give up. The chances of this are small enough that for my purposes, it's not worth worrying about. But you should be aware that it could happen. I should also note that it's not strictly necessary to use kill for the purpose I described. The ps utility can also be given a process ID with the -p option; if the process exists, the exit status will be 0, otherwise it will be greater than 0. In this case, you could also use the output to check that the name of the command matches what you expect, helping avoid the problem of a recycled process ID. In addition, ps doesn't concern itself with permissions for sending signals, so it will report on the existence of a process no matter what user you are running it as. From an efficiency standpoint, kill generally requires fewer resources to run (in fact, it is built in to some shells), but functionally ps can also do the job. So keep in mind that kill is capable of doing more than just killing off programs—maybe you can put it to one of these uses for your needs. References: Kill specification https://pubs.opengroup.org/onlinepubs/009695399/utilities/kill.html signal.h specification https://pubs.opengroup.org/onlinepubs/009695399/basedefs/signal.h.html Stopping and Restarting Apache HTTP Server https://httpd.apache.org/docs/2.4/stopping.html#hup NetworkManager: Signals https://networkmanager.dev/docs/api/latest/NetworkManager.html#id-1.2.2.9 Appendix 1 - example script: #!/bin/sh # Use your own unique name here - be sure you can write to this location pidfile="/var/run/myscript.pid" # Exit if previous run hasn't completed yet if [ -f "$pidfile" ] ; then oldpid=$(cat "$pidfile") if kill -s 0 $oldpid ; then echo "${0}: Not running script, older process $oldpid still active" exit 1 else echo "${0}: Removing old pidfile from nonexistent process $oldpid" rm -f "$pidfile" fi fi # Create pidfile echo $$ > "$pidfile" ## Insert commands to do the actual work of the script here # Remove pidfile rm -f "$pidfile" Appendix 2 - another version of the script using ps instead of kill , checking that an existing process ID is actually the same command, and with extra validation of the contents of the pidfile; perhaps better for use by a non-root user: #!/bin/sh # Use your own unique name here - be sure you can write to this location pidfile="$HOME/myscript.pid" # Exit if previous run hasn't completed yet if [ -f "$pidfile" ] ; then oldpid=$(( 1 * $(cat "$pidfile") )) if [ -n "$oldpid" ] && [ "$oldpid" -gt 1 ] ; then : else echo "${0}: Not running script, $pidfile contents invalid" exit 1 fi # Test if old process ID exists if oldcmd="$( ps -o comm= -p $oldpid )" && # Also test if command name of old process is same as current script [ "$oldcmd" = "${0##*/}" ] ; then echo "${0}: Not running script, older process $oldpid still active" exit 1 else echo "${0}: Removing old pidfile from nonexistent process $oldpid" rm -f "$pidfile" fi fi # Create pidfile echo $$ > "$pidfile" ## Insert commands to do the actual work of the script here # Remove pidfile rm -f "$pidfile" Provide feedback on this episode.

Proxmox to FreeBSD, Hidden values of CPU-Intensive Compression, Cells for NetBSD, OpenBSD 7.8 on RPIs, and more... NOTES This episode of BSDNow is brought to you by Tarsnap and the BSDNow Patreon Headlines From Proxmox to FreeBSD and Sylve in Our Office Lab The Hidden Value of CPU-Intensive Compression on Modern Hardware News Roundup Cells for NetBSD – Kernel Enforced Jail Like Isolation with User Friendly Operations OpenBSD 7.8 on Raspberry Pi Zero 2W OpenSSH 10.3/10.3p1 released I'm just the Barista Tarsnap This weeks episode of BSDNow was sponsored by our friends at Tarsnap, the only secure online backup you can trust your data to. Even paranoids need backups. Feedback/Questions Tim - Are OCI Images useful for Freebsd.md Send questions, comments, show ideas/topics, or stories you want mentioned on the show to feedback@bsdnow.tv Join us and other BSD Fans in our BSD Now Telegram channel

professorjrod@gmail.comIn this episode of Technology Tap: CompTIA Study Guide, we dive into essential Linux troubleshooting techniques vital for IT skills development and tech exam prep. Understanding how to diagnose system issues is crucial when preparing for your CompTIA exams and enhancing your practical IT abilities. We explore how to view the system as a dynamic set of processes, using tools like ps and top to monitor CPU and memory usage in real-time. Learn why process IDs (PIDs) matter and how to effectively manage problematic processes with commands like kill and kill -9. We also cover managing system services properly with systemctl to check statuses, start stopped services, and halt errant processes. Whether you're studying alone or in a study group, these insights serve as a valuable part of your CompTIA study guide, equipping you with hands-on knowledge for technology education and IT certification success.Then we zoom out to the tools that keep Linux stable in the real world: package managers like apt and dnf/yum for verified software installs, plus network troubleshooting with ping for connectivity and dig for DNS resolution. We also talk about cron automation, because scheduled tasks can be your best friend or the hidden cause of recurring issues.To round it out, we compare Linux's exposed control with macOS design choices: Finder, Dock, and Spotlight for speed, while still keeping a Unix foundation underneath. We hit key macOS security and recovery features like FileVault, Keychain, and Time Machine, and we close with CompTIA-style practice questions to lock in the concepts. Subscribe, share this with a friend studying A+, and leave a review with the command you want us to cover next.Support the showArt By Sarah/DesmondMusic by Joakim KarudLittle chacha ProductionsJuan Rodriguez can be reached atTikTok @ProfessorJrodProfessorJRod@gmail.com@Prof_JRodInstagram ProfessorJRod

La filosofía UNIX propone usar herramientas simples y especializadas que, combinadas, crean sistemas más potentes y eficaces. Enlace al post: https://www.aprendiendogtd.com/podcast-productividad/la-filosofia-unix-aplicada-a-todos-los-aspectos-de-la-vida Enlaces de interés: Ben Vallack - I Applied the Unix Philosophy to My WHOLE LIFE https://www.youtube.com/watch?v=h0s2sUu5zW0 Wikipedia - Filosofía de Unix https://es.wikipedia.org/wiki/Filosof%C3%ADa_de_Unix 6 Apps para tu sistema de productividad https://www.aprendiendogtd.com/podcast-productividad/027-6-apps-para-tu-sistema-de-productividad/ https://www.aprendiendogtd.com https://www.aprendiendogtd.com/productividad-solidaria/ Grupo Telegram: https://telegram.me/AprendiendoGTD Canal de YouTube: https://www.aprendiendogtd.com/youtube Email: info@aprendiendogtd.com Feed: https://www.ivoox.com/aprendiendo-gtd-podcast_fg_f1286811_filtro_1.xml iTunes: https://itunes.apple.com/es/podcast/aprendiendo-gtd-podcast/id1112186543?mt=2 Manolo @manolo_molero Luis @lsblasco Sergio @spantigaramos Pablo @paredes94 David @dasanru Podcast @aprendiendoGTD Sintonía: "All the Fixings" de Zachariah Hickman

Wayland setting back Linux, Dr Callahan's semi retirement, holding onto your hardware, PF queues breaking the 4gbps barrier, and mroe... NOTES This episode of BSDNow is brought to you by Tarsnap and the BSDNow Patreon Headlines Wayland set the Linux Desktop back by 10 years Semi-retirement, or, really, changing my relationship with the BSDs [Hold on to Your Hardware](https://マリウス.com/hold-on-to-your-hardware/) News Roundup PF queues break the 4 Gbps barrier Nobody said there was math on this exam! The web is bearable with RSS The Pipe Tarsnap This weeks episode of BSDNow was sponsored by our friends at Tarsnap, the only secure online backup you can trust your data to. Even paranoids need backups. Feedback/Questions Send questions, comments, show ideas/topics, or stories you want mentioned on the show to feedback@bsdnow.tv Join us and other BSD Fans in our BSD Now Telegram channel

FreeBSD and OpenZFS in the Quest for Technical Independence, Reviews make you 10x slower, OpenBSD on a Motorola 88000, Jailrun, and more. NOTES This episode of BSDNow is brought to you by Tarsnap and the BSDNow Patreon Headlines FreeBSD and OpenZFS in the Quest for Technical Independence: A Storage Architect's View Every layer of review makes you 10x slower News Roundup The story of OpenBSD on Motorola 88000 series processors Jailrun + jailrun github FreeBSD Users: We Need to Talk About Claude Code Vibe-coded ext4 for OpenBSD Tarsnap This weeks episode of BSDNow was sponsored by our friends at Tarsnap, the only secure online backup you can trust your data to. Even paranoids need backups. Feedback/Questions Send questions, comments, show ideas/topics, or stories you want mentioned on the show to feedback@bsdnow.tv Join us and other BSD Fans in our BSD Now Telegram channel

This episode originally aired on the Latent Space Podcast. swyx and Alessio Fanelli speak with Marc Andreessen about the arc of AI from its origins in 1943 to today's breakthroughs in reasoning, coding agents, and self-improvement. They cover the parallels between AI scaling laws and Moore's Law, the architectural insight behind Claude Code and the Unix shell, the coming supply crunch in compute, and why the messy reality of 8 billion people means both AI utopians and doomers are too optimistic about the pace of change. Follow Marc Andreessen on X: https://twitter.com/pmarca Follow Shawn "swyx" Wang on X:  https://twitter.com/swyx Follow Alessio Fanelli on X: https://twitter.com/FanaHOVA Listen to Latent Space. Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Fresh off raising a monster $15B, Marc Andreessen has lived through multiple computing platform shifts firsthand, from Mosaic and Netscape to cofounding A16z. In this episode, Marc joins swyx and Alessio in a16z's legendary Sand Hill Road office to argue that AI is not just another hype cycle, but the payoff of an “80-year overnight success”: from neural nets and expert systems to transformers, reasoning models, coding, agents, and recursive self-improvement. He lays out why he thinks this moment is different, why AI is finally escaping the old boom-bust pattern, and why the real bottleneck may be less about models than about the messy institutions, incentives, and social systems that struggle to absorb technological change.This episode was a dream come true for us, and many thanks to Erik Torenberg for the assist in setting this up. Full episode on YouTube!We discuss:* Marc's long view on AI: from the 1980s AI boom and expert systems to AlexNet, transformers, and why he sees today's moment as the culmination of decades of compounding technical progress* Why “this time is different”: the jump from LLMs to reasoning, coding, agents, and recursive self-improvement, and why Marc thinks these breakthroughs make AI real in a way prior cycles were not* AI winters vs. “80-year overnight success”: why the field repeatedly swings between utopianism and doom, and why Marc thinks the underlying researchers were mostly right even when the timelines were wrong* Scaling laws, Moore's Law, and what to build: why he believes AI scaling laws will continue, why the outside world is messier than lab purists assume, and how startups can still create durable value on top of rapidly improving models* The dot-com crash and AI infrastructure risk: Marc's comparison between today's AI capex boom and the fiber/data-center overbuild of 2000, plus why he thinks this cycle is different because the buyers are huge cash-rich incumbents and demand is already here* Why old NVIDIA chips may be getting more valuable: the pace of software progress, chronic capacity shortages, and the idea that even current models are “sandbagged” by supply constraints* Open source, edge inference, and the chip bottleneck: why Marc thinks local models, Apple Silicon, privacy, trust, and economics all point toward a major role for edge AI* American vs. Chinese open source AI: DeepSeek as a “gift to the world,” why open models matter not just because they're free but because they teach the world how things work, and how open source strategies may shift as the market consolidates* Why Pi and OpenClaw matter so much: Marc's claim that the combination of LLM + shell + filesystem + markdown + cron loop is one of the biggest software architecture breakthroughs in decades* Agents as the new “Unix”: how agent state living in files allows portability across models and runtimes, and why self-modifying agents that can extend themselves may redefine what software even is* The future of coding and programming languages: why Marc thinks software becomes abundant, why bots may translate freely across languages, and why “programming language” itself may stop being a salient concept* Browsers, protocols, and human readability: lessons from Mosaic and the web, why text protocols and “view source” mattered, and how similar principles may shape AI-native systems* Real-world OpenClaw use: health dashboards, sleep monitoring, smart homes, rewriting firmware on robot dogs, and why the most aggressive users are discovering both the power and danger of agents first* Proof of human vs. proof of bot: why Marc thinks the internet's bot problem is now unsolvable via detection alone, and why biometric + cryptographic proof of human becomes necessaryTimestamps* 00:00 Marc on AI's “80-Year Overnight Success”* 00:01 A Quick Message From swyx* 01:44 Inside a16z With Marc Andreessen* 02:13 The Truth About a16z's AI Pivot* 03:29 Why This AI Boom Is Not Like 2016* 06:33 Marc on AI Winters, Hype Cycles, and What's Different Now* 10:09 Reasoning, Coding, Agents, and the New AI Breakthroughs* 12:13 What Founders Should Build as Models Keep Improving* 16:33 AI Capex, GPU Shortages, and the Dot-Com Crash Analogy* 24:54 Open Source AI, Edge Inference, and Why It Matters* 33:03 Why OpenClaw and PI Could Change Software Forever* 41:37 Agents, the End of Interfaces, and Software for Bots* 46:47 Do Programming Languages Even Have a Future?* 54:19 AI Agents Need Money: Payments, Crypto, and Stablecoins* 56:59 Proof of Human, Internet Bots, and the Drone Problem* 01:06:12 AI, Management, and the Return of Founder-Led Companies* 01:12:23 Why the Real Economy May Resist AI Longer Than Expected* 01:15:53 Closing ThoughtsTranscriptMarc: Something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic. Having said that, I think what's actually happened is an enormous amount of technical progress that built up over time. And like for, for example, we now know that neural network is the correct architecture.And I, I will tell you like there was a 60 year run where that was like a, you know, or even 70 years where that was controversial. And so, so the way I think about what's happening is basically, I think, I think about basically the, the, the period we're in right now is it's, I call it 80 year overnight success, right?Which is like, it's an overnight success ‘cause it's like bam, you know, chat GPT hits and then, and then oh one hits, and then, you know, open claw hits and like, you know, these are open, these are, these are like overnight, like radical, overnight transformative successes, but they're drawing on an 80 year sort of wellspring backlog, you know, of, of, of, of ideas and thinking it's not just that it's all brand new, it's that it's an unlock of all of these decades of like very serious, hardcore research.If I were 18, like this is a hundred, this is what I would be spending all of my time on. This is like such an incredible conceptual breakthrough.swyx: Before we get into today's episode, I just have a small message for listeners. Thank you. We will not be able to bring you the ai, engineering, science, and entertainment contents that you so clearly want if you didn't choose to also click in and tune into our content.We've been approached by sponsors on an almost daily basis, but fortunately enough of you actually subscribed to us to keep all this sustainable without ads, and we wanna keep it that way. But I just have one favor to ask all of you. The single, most powerful, completely free thing you can do is to click that subscribe button.It's the only thing I'll ever ask of you, and it means absolutely everything to me and my team that works so hard to bring the in space to you each and every week. If you do it, I promise you will never stop working to make the show even better. Now, let's get into it.Alessio: Hey everyone, welcome to the Lidian Space Pockets. This is CIO, founder Kernel Labs, and I'm joined by s Swix, editor of Lidian Space.swyx: Hello. And we're in a 16 Z with a, uh, mark G and welcome.Marc: Yes, yes. A and what, half of 16? Something like that. A one. Exactly,swyx: exactly. Uh, apparently this is the, the final few days in your, your current office.You're moving across the road.Marc: Uh, we're, yeah. We have a, we have some, we have some projects underway, but yeah, this is actually, oh, this is the original. We're in actually the original office. We're in the, we're in the, we're, we're in the whole thing.swyx: It's beautiful. Yeah. Great.Marc: Thank you.swyx: So I have to come out, uh, this is a, you know, I wanted to pick a spicy start in October, 2022.I just made friends with Roone and, uh, I wanted to give him something to sort of be spicy about. And I said, uh. Uh, it'll never not be funny. The A 16 Z was constantly going. The future is where the smart people choose to spend their time and then going deep into crypto and not in ai. And that was in October 22nd, 2022.And Ruen says there was an internal meeting in a 16 Z to reorient around Gen ai. Obviously you have, but was there a meeting? What, what was that?Marc: I mean, I don't, look, I've been doing AI since the late eighties.swyx: Yeah.Marc: So I, I don't know, like all that, as far as I'm concerned, this stuff is all Johnny cum lately.Yeah. You, I mean, look, we've been doing ar entire existence. I mean, we've been doing AI machine learning deep, you know, deeply. We've been doing this stuff way from the beginning. Obviously a AI is just core to computer science. I, I, I actually view them as like quite, uh, quite continuous. Um, you know, Ben and I both have computer science degrees.Um, you know, we, we both, Ben, Ben and I actually both are world enough to remember the actual AI boom in the 1980s. Yeah. There was like a, there was a big AI boom at the time. Um, and there was a, was names like expert systems. Um, and they of like lisp and lisp machines. Uh, I, I coded in lisp. I was coding a lisp in 1989.When that was the, the language of the AI future. Um, yeah. So this is something that we're like completely, you completely comfortable with. I've been doing the whole time and are very enthusiastic aboutswyx: is there a strong, like this time is different because, uh, my closest analog was 20 16 17. It was an AI boom.Mm-hmm. And it petered out very, very quickly. Um, we, it just, it just in terms of investingMarc: sort of, sort of,swyx: yeah. Investment, investment excitement.Marc: Although that's really when the, the, the Nvidia phenomenon really, it was, I would say it was in that period when it was very clear that at, at the time it, the vocabulary was more machine learning, but it, it was very clear at that time that machine learning was hitting some sort of takeoff point.Alessio: Yeah.Marc: Well, and as you guys, you guys have talked about this at length on, on your thing, but, you know, if you really track what happened, I think the real story is, it was, it was the Alex net, uh, basically breakthrough in like 2013. That was the, that was the real knee in the curve. Um, and then it was obviously the transformer breakthrough in 17.Alessio: Yeah.Marc: Um, and then everything that followed. But, but, you know, look, machine learning, you know, there were, you know, look, uh, I mean look, I've been working, you know, I've been working with, uh, one of my, you know, kind of projects working with Facebook since 2004. Um, and on the board since 2007, and of course, you know, they, they started using machine learning very early, um, and, you know, have used it basically, you know, for like 20 years for, you know, content, you know, feed optimization and advertising optimization.And obviously many, you know, financial services. You know, many, many, many companies, many different sectors have been doing this. And so it's like one of these things, it's like, it's not a, it's not a single thing. Like it's, it's like, it's like layers, right? Yeah. Um, and, and the layers arrive at different paces and, but they kind of build up.swyx: Yeah.Marc: Uh, they kind of build up over time and then, and then, yeah. And then look, in retrospect, it was 2017 was kind of the, you know, the key, the key point with the trans transformer and then. And then as you guys know, there was this really weird like four year period where it's like the, the transformer existed and then it was just like,swyx: let's go.Yeah.Marc: Well, but, but it was just, but, but between 2020, but between 2017 and 2021, I mean, that was the era of which like companies like Google had internal chat Botts, but they weren't letting anybody use them.swyx: Yeah.Marc: Right. And then, you know, and then OpenAI developed Chat GT or GPT two, and then they told everybody, this is way too dangerous to deploy.Right. Yeah. You know, we can't possibly let normal people, normal people use this thing. And then you, you guys, I'm sure remember AI Dungeon, um mm-hmm. So the o for, there was like a year where like the only way for a normal person to use GP T three was in, in AI dungeon.Alessio: Yeah.Marc: And so you, you, we would do this, you'd go in there and you'd pretend to play Dungeons and Dragons.In reality, you're just trying to talk to talk to GPT. And so there was this, you know, there was this long, you know, and I, you know, the big, big companies, you know, big companies are cautious and, you know, the big companies were cautious. It, it, by the way, it took open ai. You know, they, they, they talk about this, it took open AI time to actually adjust, you know, kind of re redirect their researchswyx: path.I, I think, uh, let say Rosewood, right? Uh, the, the dinner that founded OpenAI was right there.Marc: Right, right. But that, that dinner would've taken place in 20swyx: 18Marc: 19. The formation of OpenAI Uhhuh as late as 2018.swyx: Uh, uh, sorry. Uh, no, I'm, I'm, I'm, I'm wrong. Probably It should be 20. Yeah. They just celebrated a 10 year anniversary, so it it is 2025.Yeah, so, so 2015?Marc: Yeah. 2015. Yeah. 2015. But then, uh, um, Alec Radford did G PT one in what, probablyswyx: mm-hmm. 17, 18,Marc: yeah. 17, 18. So it, yeah. For, and then, and then they didn't really, and then GPT three was what? 2020? 2020.swyx: 2020.Marc: Because that became copilot immediately. Even open ai, which has been, you know, the leader of, of this thing in the last decade, you know, e even they had to adapt and, and, and lean into the new thing.And so. Um, yeah, I, I think it's just this process of basically sort of wave after wave layer after layer, you know, building on itself. And then you kind of get these catalytic moments where, where the whole thing pops and, and obviously that's what's happening now.swyx: Is it useful to think about will there be any ai, winter?‘cause there's always these patterns. Like, is this, in the summer is something I constantly think about because do I get, do I just like. Just get endlessly hyped and just trust that I will only be early and never wrong or right. Well, are we, will there be a winter?Marc: So there's something about, say the following.There's something about AI that has led to this repeated pattern. Um, and, and, and you guys know this,swyx: it's summer, winter, summer,Marc: winter, summer, winter, summer, winter. And it goes back 80 years. Yeah. 80 years. Uh, so the original neural network paper was 1943. Right. Which is, which is amazing. Uh, that it was, it was far back that long.And then there was you, if you guys have ever talked about this on your show, but there was this, uh, there was a big, uh, there was an a GI conference at Dartmouth University in 1950. 55. 55, yeah. And they got a NSF grant to, uh, for the, all the AI experts at the time to spend the summer together. And they figured if they had 10 weeks together, they could get a GI, uh, at the other end.And they got their, by the way, they got the grant, they got the 10 weeks and then, you know, 1955, you know. No, no. A GI. And like I said, I, I lived through the eighties version of this where there was a big, a big boom and a crash. And so, so there is this thing, and there, there is something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic.Um, and, and it's probably on both sides of like the, the, the boom bus cycle. You, you kind of see that play out. Having said that, I think what's actually happened is like just, and you know, and we now know in retrospect like an enormous amount of technical progress that built up over time. And like for, for example, we now know that neural network is the correct architecture.And I, I will tell you like there was a 60 year run where that was like a, you know, or even 70 years or that was controversial. And, and we now know that that's the case. And so we, we now, you know, everything we're building on today just sort of derives from the original idea in 1943. And so, so in retrospect, we, we now know that like, these, these guys are right.They, they, you know, they would get the timing wrong and they thought, you know, capabilities would arrive faster, or they were, it could be turned into businesses sooner or whatever, but like, they were fundamentally, the, the scientists who worked on this over the course of decades were fundamentally correct about what they were doing.And, and the, and the payoff from, from, from all their work is happening now. And so, so the way I think about what's happening is basically, I think, I think about basically the, the, the period we're in right now is it's, I call it 80 year overnight success, right? Which is like, it's an overnight success.‘cause it's like bam, you know, chat, GPT hits and then, and then oh one hits, and then, you know, open claw hits and like, you know, these are open, these are, these are like overnight, like radical, overnight transformative successes, but they're drawing on an 80 year sort of wellspring backlog, you know, of, of, of, of ideas and thinking it's not just that it's all brand new, it's that it's an unlock of all of these decades of like very serious, hardcore research.Um, and thinking, and look, there were AI researchers who spent their entire lives. They got their PhD. They, they worked for, they've researched for 40 years. They retired in a lot of cases, they passed away and they never actually saw it work.swyx: Yeah. It's all sad.Marc: It is. It is sad. It's sad. Knewswyx: Jeff Hinton was like the last guy.Marc: Yeah. Yeah. Well, there were the guys, uh, was a guy, Alan Newell. I mean, there's tons of John McCarthy. You know, John McCarthy was like one of the inventors in the field. He's one of the guys who organized the Dartmouth Conference and you know, he taught at Stanford for 40 years. Wow. And passed, you know, passed away, I don't know, whatever, 10, 10 years ago or something.Never, never actually go. Got to see it happen. But like, it is amazing in retrospect, like, these guys were incredibly smart and they worked really hard and they were correct. So anyway, so then it's like, okay, you know, say history doesn't repeat, but it rhymes. It's like, okay, does that mean that there's gonna be another, like, you know, basically boom buzz cycle.And I, I will tell you, like, let, like in a sense, like yes, everything goes through cycles and, you know, people get overly enthusiastic and overly depressed and there's, there's a time, there's a timelessness to that. Having said that, there's just no question. Um, so the form, the foremost dangerous words in investing this time are, this time is different.Do you know the 12 most dangerous words investing? No. The four most d foremost dangerous words in investing are this time is different. Yeah. Um, the 12 most dangerous words. And so like, I'll tell you what's different. Like now it's working like, like there's just no, I mean, look, there's just no question.And by the way, I, I'll just give you guys my take. Like L LLMs, like from, from basically the Chad G PT moment through to spring of 25. I think you could still, I think well intention, well, and of. Form skeptics could still say, oh, this is just pattern completion. And oh, these things don't really understand what they're doing.And you know, the hall hallucination rates are way too high. And, you know, this is gonna be great for creative writing and creating, you know, Shakespeare and so sonnets and, you know, as, as rap lyrics or whatever, like, it's gonna be great and all that stuff, but we're not gonna be able to harness this to make this relevant in, you know, coding or in medicine or in law or in, you know, you know, kind of feels that, you know, kind of really, really matter.And I think basically it was the reasoning breakthrough. It, it was oh one and then R one that basically answered that question basically said, oh no, we're gonna be able to actually turn this into something that's gonna work in the real world. And, and then obviously the coding breakthrough over the, over basically the coding breakthrough that kind of catalyzed over the holiday break was kind of the third step in that.Mm-hmm. Where you're just like, alright, if, if, you know, if Linus Tova is saying that the AI coding is no better than he is like. Like, that's, that's never happened before. That's theswyx: benchmark.Marc: Yeah. That's never happened before. And so now we know that it's, it's gonna sweep through coding and, and then, and then we, we know, you know, we know that if it's gonna work in coding, it's gonna work in everything else.Right. It's just then, because that's, that's like, that's like, that's like the hardest in many ways. That's the hardest example. And how everything else is gonna be a, a derivative of that. And then on top of that, we just got the agent breakthrough, you know, with Open Claw, which is fantastic. Which is amazing and incredibly powerful.And then we just got the, the, um, the auto research, uh, you know, the, the self-improvement. You know, we're now into the self-improvement breakthrough. And so the, so the way I think about it is we've had four fundamental breakthroughs in functionality, l OMS reasoning, uh, agents, um, and then, uh, and, and then now RSI, um, and, and they're all actually working.Um, and so I'm, I'm just, as you like, you can tell I'm jumping outta my shoes. Like, like this is, like this is it like this, this is the culmination of 80 years worth of worth of work, and this is the time it's becoming real.Alessio: Yeah.Marc: I, I'm completely convinced.Alessio: I think the anxiety that people feel is like during the transistor era, yet Mors law, and it's like, all right, we understand why these things are getting better.We understand the physics of it. Yeah. With ai, it's. It's so jagged in like the jumps where like, like you said, it's like in three months you have like this huge jump like, and people are like, well this can keep happening. Right? But then it keeps happening,Marc: it'll keep happening.Alessio: And so like how do you think about also timelines of like what's we're building?I think we always have this question with guests, which is like, you know, should you spend time building harness for a model versus like the next model just gonna do it one shot in the lead space. Right. And how does that inform, like how you think about the shape of the technology? You know, you talk about how it's a new computing platform.If you have a computing platform, then like every six months it like drastically changes in what it looks like. It's hard to build companies on top of it.Marc: Yeah. So, so a couple things. So one is like, look, the, the Moore's law was what we now call a scaling law. Like Moore's Law was a scaling law and for your younger viewers, more Moore's Law was every chip chip chips either get twice as powerful or twice as cheap every, every 18 months.And that, and that and that, you know, that it's gotten more complicated in the last few years. But like that, that was like the 50 year trajectory of, of, of the computer industry. And then, and then by the way, and that's what took the mainframe computer from a $25 million current dollar thing into, you know, the phone in your pocket being, you know, a million times more powerful than that.Like that, you know, for, for 500 bucks. And so that, that was a scaling law. And then, and then, and then key to any scaling law, including Moore's Law and the AI scaling laws is, you know, they're not really laws, right? They're, they're, they're, they're predictions, but when they work, they become self-fulfilling predictions because they, they, they, they, they set a benchmark and, and then the entire industry, right?All the smart people in the industry kind of work to make sure that, that, that actually happens. And so they, they kind of motivate the breakthroughs that are required to, to keep that going. And, and in and in chips, that was a 50 year, that was a 50 year run. Right. And it, it was amazing. And it's still happening in, in some areas of, of chips.I think the same thing is happening with the, the core scaling laws. The core scaling laws. In, in, in ai, you know, they're, they're not really laws, but like they, they are basically. There are predictions and then they're motivating catalysts for the research work that is required to be. And, and, and, and by the way, also the investment, uh, dollars, um, uh, you know, required to basically keep, you know, keep the curves going and, and look, it, it is, it's gonna be complicated and it's gonna be variable and they're, you know, there're gonna be walls that are gonna look like they're fast approaching, and then they're gonna be, you know, engineers are gonna get to work and they're gonna figure out a way to punch through the walls.And obviously that's, you know, that's been happening a lot, you know, and then look, there's gonna be times when it looks like the walls have, you know, the, the, the laws have petered out and then they're gonna, they're gonna pick up again and surge and then, and then, and then it, it appears what's happening to the eyes is there's not multiple, you know, multiple scaling laws.Um, there's multiple areas of improvement. And, and I think, you know, I don't know how many more there are already yet to be discovered, but there are probably some more that we don't know about yet. You know, they, like, for example, there's probably some scaling law around, um, world models and robotics that we don't fully understand, you know, kind of acquisition of data at scale in the real world that we don't fully understand yet.So that, that, that one will probably kick in at some point here. There's a bunch of really smart people working on that. Um, and so, yeah, I, I think the expectation is that, that, you know, the, the scaling laws generally are gonna continue. Yeah. The, the pace of improvement will continue to move really fast.Um. To your question on like what to build. So, uh, I'm a complete believer the scaling laws are gonna continue. I'm a complete believer the capabilities are gonna keep getting amazing, um, you know, leaps and bounds. Uh, the part where I kind of part ways a little bit with how, what I would describe as the AI purists, um, you know, which is, which I would characterize as like the people who are.In many ways, the smartest people in the field, but also the people who spend their entire life, like at a lab, um, and have, have, I would say, have very little experience in the outside world. Um, the, the, the nuance I would offer is the outside world of 8 billion people and institutions and governments and companies and economic systems and social systems is really complicated.Um, and, um, and doesn't, you know, it it 8 billion people making collective decisions on planet Earth is not a simple process of like, just like you see this happening now. It's like a bunch of AI CEOs have this thing, which is just like, well, there's just this, they just all have this kind of thing when they talk in public where they're just like, well, there's these, these obvious set of things that so society to do.Alessio: Mm-hmm.Marc: And then they're like, society's not doing any of those things. Right. And it's like, how can society not, you know, what, whatever their theory is, how can society not see x, y, Z? Mm-hmm. And the answer is, well, society is number one. There's no single society, it's like 8 billion people. And they like all have a voice, and they all have a vote, like at the end of the day of how they, they react to change.And then, you know, it just like, it's just human reality is just really complicated and messy. Um, and, and, and so the specific answer to your question is like, as usual, it depends. Um, you know, it, it depends. Look, pe there's no question people are gonna, like, there's no question they're gonna be companies.It's already happening. There are companies that think that they're building value on top of the models and then they're just gonna get blissed by the, by the next model. There's no question that's happening. But I think there's no question also that just the process of adaptation of any technology into the real and into the real messy world of humanity is, is just going to be messy and complicated.It's, it's not going to be simple and straightforward. It's gonna be messy and complicated. And there are gonna be a lot of companies and a lot of products, um, uh, and in, in fact entire industries that are gonna get built to, to, to basically actually help all of this technology actually reach real people.Alessio: The amount of capital going into these companies, I mean, Dario talked about it on the Door Cash podcast and Door Cash was like, why don't you just buy 10 x more GPUs? And he is like, because I'm gonna go bankrupt if the model doesn't exactly hit the, the performance level. How do you think about that?Also as a risk on, you know, you guys are investors, open AI and thinking machines and world apps. It seems like we're leveraging the scaling loss at a pretty high rate, right? Like how comfortable, I guess, do you feel with the downside scenario, like, and say like things Peter out, you think you can kind of like restructure uh, these build outs and uh, you know, capital investments.Marc: Yeah. So should start by saying, so I live through the.com crash, um, and I can tell you stories for hours about the.com crash and it was horrible. No, it was awful. It was, it was, it was apocalyptic by the way. The, a lot of the.com crash was actually at the time, it was actually a telecom crash. It was a bandwidth crash.Like the, the thing that actually crashed, that wiped out all the money with the tele, the telecom companies.swyx: GlobalMarc: crossing. Global, global, yeah.swyx: I'm from Singapore and they, they laid so much cable o over over our oceans.Marc: Actually there was a scaling law in the.com. Era. And it was literally the, the US Commerce Department put out a report in 1996 and they said internet traffic was doubling every quarter.Um, and, and actually in 1995 and 1996, internet traffic actually did double every quarter. And so that became the scaling law. And so what all these telecom entrepreneurs did was they went out and they raised money to build fiber, anticipating that the demand for bandwidth is gonna keep doubling every quarter.Doubling every quarter though is like, you know, grains of chess and the chessboard, like at some point the numbers become extremely large. Right. And, and, and it really, and really what happened was the internet. The internet by the way, continuously kept growing basically since inception. And it's, you know, it's, it's continuously grown.It's never shrunk. And it's grown really fast compared to anything else. Mm-hmm. You know, in, in, in human history. But it wasn't doubling every quarter as of 19 98, 19 99. And so there was this gap in the expectation of what they thought was a scaling law versus reality. And that's actually what caused the.com crash, which was the, it they, they way over companies like global crossing way overbuilt fiber, which is sort of the, and by the way, fiber, telecom equipment, you know, so all the, all the networking gear, you know, and then, and then by the way, the actual physical data centers, like that was the beginning of the, of the, of the data center build and then, and the data center overbuild.And so you had that, but it was, it was literally, I think it was like $2 trillion got wiped out, right? It was like Jesus, it was like a big, it was. And by the way, the other, the other subtlety in it was the internet companies themselves never really had any debt. ‘cause tech, tech companies generally don't run on debt, but the telecom companies run on debt.Physical infrastructure companies run on debt. And so the companies like Global Crossing not just raise a lot of equity, they also raise a lot of debt. So they're highly levered. And so then you just do the thing. It's just like, okay, you have a highly levered thing where you're, you're just over, you're overbuilding capacity.Demand is growing, but not as fast as you hoped. And then boom, bankrupt. Right. And, and then it, and then it's like they say about the hotel industry, which is, it's always the third owner of a hotel that makes money. It has to go bankrupt twice, right? You have to wash out all of the over optimistic exuberance before it gets to actually a stable state.And then it makes money. So by the way, all of those data centers and all of those, all the fiber that they're in use, it's all in use today. Yeah. But 25 years later. But it, it, it took, and actually the elapsed time was, it took 15 years. It took 15 years from 2000 to 2015 to actually fill, fill up all that capacity.The cautionary warning is the, the overbuild can happen. Um, and, and, and, and, you know, you, you get into this thing where basically everybody, everybody who basically has any sort of institutional capital, it's like, wow. It's just, I, I don't know how to invest in these crazy software things. For sure I can put build data centers and for sure I can buy GPUs that I can deploy, you know, compute grids and, and all these things.Um, and so, you know, if you're a pessimist, you could look at this and you could say, wow, this is like really set up to be able to basically replicate, you know, what we went through, what we went through in 2000. Obviously that would be bad. The counter argument, which is the one I I agree with, which is the counter on, on the other side is a couple things.One is the companies that are investing all the, the companies that are investing the money are like the bluest chip of companies. And so back, back, back in the, in the do, like Global Crossing was like a, it was like an entrepreneur. It was like a, a new venture, but like the money that's being deployed now at scale is Microsoft, and, you know, and Amazon and Google, Facebook and Facebook and Nvidia and, you know, these, these, these, and, and now you know, by the way, open ai philanthropic, which are now at like, you know, really serious size, um, you know, as companies with, you know, very serious revenue.These are very large scale companies with like, lots, lots of cash, lots of debt capacity that they've, they've never used. And so th this is institutional in a way that, that really wasn't at the time. And then the other is, at least for now, every dollar that's being put into anything that results in a running GPU is being turned into revenue right away.Like so, and you guys know this, like everybody's starved for capacity, everybody's starved for compute capacity and then, you know, all the associated things, memory and, and, and interconnected and everything else. Um, data center space. And so e every dollar right now that's being put into the ground is turning into revenue.And, and it, and in fact, I actually think there's an interesting thing happening, which is because everybody starve for capacity, the models that we actually have that we can use today are inferior versions of what we would have if not for the supply constraints. That's true. Um, if Right pose a hypothetical universe in which GPUs were 10 times cheaper and 10 times more plentiful mm-hmm.The models would be much better. ‘cause you would just allocate a lot more money to training and you'd just build better models and they would be better. Um, and so we're, we're actually getting the sandbag version of the technology.swyx: Yeah. No. Everything we use is quantized because the, the labs have to keep the, the full versions,Marc: right?swyx: LikeMarc: we're not even getting the good stuff.swyx: Yeah.Marc: But, but getting the good stuff, it's, it's just, even if technical progress stops. Once there's like a much bigger build of like GPU manufacturing capacity and memory, you know, all, all the things that have to happen in the course of the next five or 10 years.Once it happens, even the current technology is gonna get, gonna get much better. And then as you know, like there's just like a million ways to use this stuff. Like there's just like a million use cases for this. Mm-hmm. Like, it, it, you know, this isn't just sending packets across a, a thing, whatever, and hoping that people find something to do with it.This is just like, oh, we apply intelligence into every domain of human activity. And then it works like incredibly well. Yeah. Um. Here's what I know, here's what I know. Um, in the next three or four year, it's like somewhere between three or four years out, basically everything is selling out. So like the, the entire supply chain is, is, is, is sold out or, or, or selling out.And so there, there's no, like, we're just gonna have like chronic supply shortage for, you know, for years to come. Um, there's going to be a response from the market that's gonna result in an enormous, you know, it's happening now. An enormous flood of investment in a new fab capacity and ev you know, every, everything else to be able to do that, at some point the supply chain constraints will unlock, you know, at least to some degree that will be another accelerant to industry growth when that happens.‘cause the products will get better and everything will get cheaper. Um, and so, so I know that's gonna happen. I know that, you know, the deployments, you know, the, the actual use cases are like really compelling. And then, like I said, you know, with reasoning and agents and so forth, like, I know they're just gonna get like much, much better from here.And so I, I, I know the capabilities are like really real and serious. I also know that the technical progress is not going to stop. It. It, it is excel. It is, is accelerating. Like the, the breakthroughs are are tremendous. I mean, even just month over month, the breakthroughs are really dramatic. And so, you know, I think if you were a cynic and there, there are cynics, you can look at 2000, you can find echoes.But I can't even imagine betting it that this is gonna like somehow disappoint and, you know, at least for years to come, I think it would be essentially suicidal to make that bet. Yeah. Um, it was that Michael Burry, uh, uh, that'sswyx: anMarc: interesting guy, huh? We'll pick on a guy. We'll pick, let's pick on one guy.We'll pick. Well ‘cause he did, he he came out with, it was, it was the, heswyx: doesn't mind.Marc: It was the Nvidia short. Right. He came with the Nvidia short. And then if you guys probably talked about this, which is the, the analysis now that like the current models are getting better faster at such a rate that if you are running an Nvidia, if you're running an Nvidia inference chip today, that's three years old, you're making more money on it today than you did three years ago because the pace of improvement of the software is, is faster than the, the, the depreciation cycle, the chip.And then my understanding is Google is running. I don't if they've, I don't know exactly what, uh, these are rumors that I've heard or maybe it's public, but, um, I think Google's running very old TPUs, very profitably. Ference. Yeah. And very profit and very profitably. Yeah. Um, and so, so it actually turns out, as far as I can tell, it's actually the opposite of the Beery thesis is actually.He was actually 180 degrees wrong. It's actually the, the, the, the old Nvidia chips are getting more valuable, which is something that's like literally never happened before. Like it's never been the case that you have an older model chip that becomes more valuable, not less valuable. And that, and again, that's an expression of the just ferocious pace of software progress.Ferocious pace of capability payoff. Yeah. Uh, that you're getting on the other side of this. And so I just, the idea of betting against that, like.swyx: Yeah. Yeah. Well, one ofMarc: my, it seems like an invitation to get your face ripped up.swyx: One of my early hits was like modeling the lifespan of the H 100 and h two hundreds and, and going like, you know, usually they advise like four to seven years and it was, you know, maybe you sort of realistically haircut cut it down to two to three.Yeah. But actually it's going up and not down. Yeah. And, and uh, that's, I mean that's, I think that's the dream. Uh, we are finding utilization and I think utilization solves all problems. Like, you can, you can find use, use cases for even like the poor, like even memory, we're having a shortage. Right. And, and even like the, the shittier versions of, of memory that we do have, we are finding use cases for it.So like That's great.Marc: Yeah.Alessio: How, how important is open source AI and kinda like edge inference in a world in which you have three years of supply crunch. Like, do you think in the, like, you know, if you fast forward like five years, like how do you think about inference, uh, in the data center versus at the edge?Marc: Well, so just to start, yeah. So I think, I think open source is very important for a bunch of reasons. I think edge, edge inference is very important for a bunch of reasons. I, I think just practically speaking, if we're just gonna have fundamental construc, supply crunches for the next, I mean, you, you guys know if you just project forward demand over the next three years, right?Yeah. Relative to supply, one of the, its main predictions you can do is what's gonna, what, what's gonna happen to the cost of, of inference in the core, uh, over the next three years? And like, it may rise dramatically, right? Like, so, so what is, and then is, is, you know, like the, the, the big model competition are subsidizing heavily right now.Right? Right. And so, so what's the, what will be the average person's, you know, per day, per month token cost, you know, three years from now to do all the things that they want to do. And I, I don't know, it's gonna. I mean, I have, you guys probably have friends, I have friends today who are paying a thousand dollars a day for open claw, for claw tokens to run open claw.Right? And so, okay. $30,000 a month. Right? And, and by the way, those, those friends have like a thousand more ideas of the things that they want their claw to do, right? Yeah. And so you, you could imagine there, there's like latent demand of up to, I don't know, five or $10,000 a day of, of, of tokens for a fully deployed, you know, per personal agent.Uh, and obviously consumers can't pay that, right? And so, so, but it gives you a sense of the fu of the fu of the future scope of demand, right? And so, so even, even if there's a 10 x improvement in price performance, that still, you know, goes to a hundred dollars a day, which is still way beyond what people can pay.Mm-hmm. So there's just gonna be like. Ferocious to me, by the way. The agent thing, the other interesting thing is I think the agent thing, so up until now, a lot of the constraints of GGPU constraints, I think the agent thing now also translates into CPU constraints. Mm-hmm. Right?swyx: CPU memory.Marc: Yes. CPU memory, right?And so, like the entire chip ecosystem is just gonna get wait,swyx: wait for network constraints, that that will be the killer.Marc: It's all bottleneck potentially for years. And so, so I, I think that Brad, and, and I think it's actually possible, I mean, generally inference costs are gonna keep coming down, but I think the, let's put it this way, the rate of decline, I think may level out here for a bit because of these supply constraints.And then at some point, maybe the lab stops subsidizing so much and that, that, that again, will be, be an issue. And so there's just gonna be so much more demand for inference than, than can be satisfied. Um, you know, kind of with the centralized model. And then, and then, you know, you guys know this, but like all the, just the dramatic, I mean just the dramatic innovations that have happened in the Apple silicon to be able to do, uh, inferences, it's quite amazing the level of effort being put.Like the open source guys are putting incredible effort into getting, you know, this recurring pattern where the big model will never run on a pc, and then six months later mm-hmm. Oh, it runs in a pc, right? It's like amazing. And there's very smart people working on that. So there's all that. And then look, there's also, you know.There's also like other, there's other motivators. There's other motivators which is just like, okay, how much trust are the big centralized model providers? You know, how much trust are they building in the market versus, you know, how much are, you know, at least for, in certain cases with some people, for certain use cases, people being like, well, I'm not willing to just like, turn everything over.So there, there, there's all the trust issues. Um, by the way, there's also just like straight up price optimization. There's many uses of AI where you don't need Einstein in the cloud. You just need like a, a a, a smart local model. There's also performance issues where you want, you know, you want, you know, you're gonna want your doorknob to have an AI model in it.Right. You know, to be able to, you know, do, um, you know, to be able to do access control. Um, obviously like everything with a chip is gonna have an AI model in it. Mm-hmm. And it, a lot of those are gonna be local. Um, and so, yeah. No, like I think, I think you're gonna have ti and then you're gonna, by the way, also wearable devices, you know, you don't wanna do a complete round trip.You want, you know, you, whatever your smart devices are, you want it to be like super low latency. Yeah.swyx: The question, do we care who makes it? Yeah. One of the biggest news this week was the collapse of AI two, the Allen Institute. Mm-hmm. One of the actual American open source model labs. Yeah. Um, and, uh, I'm not that optimistic on, on American open source.Yeah. Like you, you guys invested in MIS trial and MIS trial's doing extremely well outside of China. That's about it.Marc: Yeah. We'll see. We'll see. I look, I, number one, I do think we care. Uh, I do think we, I do think we care who makes it. Um, I would say this, the, the, the, the previous presidential administration wanted to kill it in the us Oh yeah.They wanted to drown in the bathtub. Um, and so they wanted to kill it. So at least we have a government now that actually like, actually wants it wants it to happen. And youswyx: earned to councilMarc: and Yeah. And the new and the P pcast. Yeah. So the, the, you know, this admin for whatever other political issues people have, which are many, you know, this administration has, I think a very enlightened view and in particular an enlightened view on AI and in particular on open source ai.Uh, and so they're very supportive. Um, my read is the Chi. The Chinese have a very, the various Chinese companies have a very specific reason to do open source, which is, they, they, they don't fundamentally, they don't think they can sell commercial, uh, AI outside of China right now. And or at least specifically not, not in the US for a combination of reasons.And so they, they kind of view, I think, open source AI as a bit of a loss leader against basically domestic, uh, you know, paid, paid services. And then kind of an, you know, kind of an ancillary products. You know, they're, they're very excited about it, by the way. I think it's great. I think it's great that they're doing it.Um, you know, I think Deeps seek was like a gift to the world. Um, I think. The great thing about open source, open source, the, the, the impact of open source is felt two ways. One is you, you get the software for free, but the other is you get to learn how it works, right? And so like the paper, the paper, the paper and, and the code, right?And the code. And so, like, for example, I thought this was amazing. So open comes out with L one and it's an amazing technical breakthrough, and it's just like, absolutely fantastic. But of course they don't explain how it works in detail. And then of course they hide the, they hide the reasoning traces, right?And, and then, and then, and then everybody's like, okay, this is great, but like, who's gonna be able to replicate this? Are other people gonna be able to do this? You know, is their secret sauce in there? And then our one comes out and it's just like, there's the code and there's the paper, and now the whole world knows how to do it.And then, you know, three months later, every other AI model is, is adding reasoning. And so, so you get this kind of double, like even if the Chinese models themselves are not the models that get used, the education that's taken place to the rest of the world, the information diffusion, you know, is incredibly powerful.So that happens and then, I don't know. We'll, we'll see. You know, there are a bunch of American, you know, open source, you know, ai, uh, model companies. I mean, look, there's gonna be tremendous, you know, there already is. There's, you know, there's gonna be tre there's tremendous competition, uh, among the primary model companies.You know, there's, depending on how you count, there's like four or five, you know, big co model companies now that are, you know, kind of neck and neck, uh, in different ways. Um, uh, you know, and, and, and, um, you know, and then obviously Bo Bo both X and then MetAware involved are, you know, both have huge, you know, huge attempts to, you know, kind of, to kind of leapfrog underway.And then you've got, you know, a whole fleet of startups, new companies, including a whole bunch that we're backing, that are, you know, trying to come out with different approaches. And then you've got whatever it is. I don't know how, how many, how many, like main line foundation model companies are there in China at this point?It's probably six. It'sswyx: five Tigers is what they call it. Yeah. Uh, Quinn is in questionable because there's change in leadership,Marc: right?swyx: Yeah.Marc: But that, does that include, that includes like Moonshot,swyx: yes. Can deep seek, uh, uh, ZI, um, Quinn oh one is in there.Marc: Right. And then, um, and by dance and, and then you see,swyx: ance would be like the next tier ance.They weren't as prominent. They weren't, didn't haveMarc: a leading. Yeah. But they, you at least, you know, ance is very inspiring and presumably they have more stuff coming and Tencent probably has more stuff coming and, and so forth. And so, so, so like, look, here, here would be a thing you can anticipate, which is there are not these markets, there are not going to be between the US and China right now, there's like a dozen primary foundation model companies that are like at scale, at, at some level of a critical mass.It's not gonna be a dozen in three years, right? Like, it just because these industries don't bear a dozen, it's, it's gonna be three or you know, there's gonna be three or four big winners or maybe one or two big winners. And so there's gonna be like a whole bunch of those guys that are gonna have to figure out alternate strategies.Um, and I think like open source is one of those strategies. And so I, I think you could see like a whole, i, I, I think the questions like, who's gonna do open source? I think that could change really fast. I, I think that, that, that's a very dynamic thing. I think it's very hard to predict what happens. And, and I think it's very important.swyx: NVIDIA's doing a lot.Marc: Well, I was gonna say. Well, exactly. And then you're got Nvidia and then, and then, you know, just to, again, indu, there's an old thing in business strategy, which is called, uh, commoditize Compliments. Commoditize the compliment. That's right. And so if your Jensen is just kind of obvious, of course, you wanna commoditize the software.Yeah. And he's, and to his enormous credit, he's putting enormous resources behind that. And so maybe it, maybe it's literally Nvidia and I think that would be great.Alessio: Yeah. Uh, narrative violation to European projects, uh, in the, uh, damn.swyx: I'm hosting my, uh, Europe, uh, conference soon. And I got both of them.Alessio: They got us.They got us. MarkMarc: finished. They got us, us. Well, wait a minute. Where was Peter? So where was Steinberger when he did? In AustriaAlessio: was, yeah, yeah, yeah.Marc: He was in what? He was in Vienna. Oh, he was in Vienna. And then where is he now?swyx: Uh, he's moving to sf.Marc: Okay. Okay. Alright. Okay, there we go. And then, yeah, the PI guy, right?The PI guys are European.swyx: Yeah, they're also, they're buddies inAlessio: Australia. Mario's also there. Yeah.Marc: Right. And are they, yeah, they haven't announced yet. Any sort of change changed or have theyAlessio: No, they're, they have a company there.Marc: Okay. Got, okay. Good.Alessio: Good, good,good.Alessio: Um,Marc: yeah, good.swyx: Anyways, I think pie and open cloud very important software things and, and I just wanted you to just go off on what you think.Marc: Yeah. So I think in co the, the combination of the two of them I think is one of the 10 most important softwares. Openswyx: Claw got all the attention, but Right. Talk about pie,Marc: pi pie's, kind of the Yeah. PI's, PI's kind of the architectural breakthrough for those of us who are older. There was this whole thing that was very important in the world of software basically from like 1970 to, I don't know, it still is very important, but like 19, from 1973 to like basically the creation of Linux, which is basically this, this thing used to call like the Unix mindset.Like so, so, ‘cause there were all these different, you know, theories. There are all these different operating systems and mainframes and, and then you know, all these windows and Mac and all these things. And then there was this, but kind of behind it all was this idea of kind of the Unix mindset. And the Unix mindset was this thing where basically you don't have these, like, like in the old days, like, like the operating system that like made the computer industry really work, like in the 1960s mm-hmm.Was this thing called o os 360, which was this big operating system that IBM developed that was supposed to basically run everything. And it was this like giant monolithic architecture in the sky. It was like a, you know, it was like a giant castle. Um, of software. And, and by the way, it worked really well and they were very successful with it.But like, it was this huge castle in the sky, but it was this thing, it was almost unapproachable, which is like, you had to be kind of inside IBM or very close to IBM. And you had to really understand every aspect, how the system worked. And then the, the Unix sky is originally out of at and t and then out out of Berkeley, um, you know, came out and they said, no, let's have a completely different architecture.And the way architecture's gonna work is we're gonna have, we're gonna have a, a prompt and, and a, and a shell. And then, and then we're gonna, all, all the functionality is gonna be in the form of these discreet modules, and then you're gonna be able to chain the modules together. Mm-hmm. Yeah. And so like the, the, the op, it's almost like the operating, operating system itself is gonna be a programming language.Um, and then that led led to the, the, the sort of centrality of the shell. Um, and then that led to sort of, uh, you know, basically chaining together Unix tools. And then that led to the emergence of these, these scripting languages like Pearl, where you, you could basically kind of very easily do this, and then the shells got more sophisticated and then, and then, and then look like, you know, that, that, that number one, that worked and that, that was the world I grew up in.Like I was, I was a Unix guy. You know, sort of from, call it 1988 to, you know, kind of all, all the way through my work and it worked really well. It, it's in the background, um, you know, nor normal people don't need to, didn't need to necessarily know about it, but like, if you were doing like system architecture, application development, you, you, you knew all about it.Um, and then, you know, it's been in the background ever since. And, you know, look, your Mac still has a Unix shell, you know, kind of in there, and your iPhone still has a Unix shell kind of buried in there somewhere. So they're kind of in there. And then, you know, the Windows shell is kind of a, you know, sort of a weird derivative of that.But, um, you know, but look, the inter, the internet runs on Unix, um, and that smartphones, actually, both iOS and Android are Unix derivatives. And so, you know, kind of Unix did end up winning. But, but anyway, and then we just started taking that for granted. And then, and then so, so basically the, the way I think about what happened with Pie and then with Open Claw is basically what those guys figured out is, I always say the, the great breakthroughs are obvious in retrospect, right?Which is the best kind, the best kind. They weren't obvious at the time or somebody else would've done them already. Um, and so there is a, like a real conceptual leap, but then you look at it sort of the backwards looking and you're just like, oh, of course. Mm-hmm. Like the, the, to me those are always the best breakthroughs.Well, actually language models themselves are like that. It's just like, oh, next token completion. Oh, of course.swyx: Yeah. What other objective mattered?Marc: Yeah, exactly. But, but like it, right. But she's even saying it wasn't obvious until somebody actually did it. Right. And so the conceptual breakthrough is real and deep and powerful and, and very important.And so the way I think about pie and olaw is it's basically marrying the, the language model mindset to the un to the Unix, basically shell prompt mindset. And so it's, it's basically this idea that what, what, so what is an agent, right? And as, as, and as you know, like many smart people who have been trying to figure out what an agent is for, for, for decades, and they've had many architectures to build agents and the whole thing.And it turns out what is an agent. So it turns out what we now know is an agent is the following. It's, so it's a language model. And then above that, it's a ba, it's a bash shell. Um, so it's a, it's a Unix shell, and then it's, and then the agent has access, uh, has access to, to the shell. And, you know, hopeful, hopefully in a sandbox, maybe in, maybe in a sandbox.So it's, it's the model. Um, it's the shell. Um, and then it's a fi, it's a file system. Um, and then the state is stored in files. And then, you know, there's the markdown format for the, you know, for, for the files themselves. And then, and then there's basically what in Unix is called Aron job. There's a loop and then there's a heartbeat for the, there's heartbeat and, and the thing basically Wake Wakes up.Wakes up. So it's basically LLM plus shell, plus file system, plus markdown, plus kron. And it turns out that's an agent. And, and, and every part of that, other than the model is something that we already completely know and understand. And in fact, it turns out that like the latent power of the Unix shell is like extraordinary because basically like all, like, there's just like an, there's just enormous latent power in the shell.There's enormous numbers of Unix commands, there's enormous number of command line interfaces into all kinds of things already in the, you know, your entire, I mean your entire, just to start with, your computer runs on a shell. If you're running a Mac or a, or, or a phone, your computer, your computer's running on a shell, uh, already.And so like the full power of your computer is available at the command line level. Um, and then it turns out it's really easy to expose other functions as a command line interface. And so like this whole idea where we need like MCP and these like product mm-hmm. Fancy protocols, whatever, it's like, no, we don't, we just need like a command, command line thing.So that's the architecture. And then it turns out what is your agent? Your agent has a bunch of files starting a file system. And then there's the thing that just like completely blew my mind when I write my head around it as a result of this, which is like, okay. This means your agent is now actually independent of the model that it's running on.Because you can actually swap out a different LLM underneath your agent and your, your agent will change personality somewhat. ‘cause the model is different, but all of the state stored in the files will be retained.swyx: Yeah. Different instruction set, but you just compiledit.Marc: Right, exactly. And it's all right.It's like right. Swapping out a ship and recompiling, but it's, it's still, it's still your agent with all of its memories. Um, and with all of its capabilities. And then by the way, you can also swap out the shell, uh, so you can move it to a different execution environment that is also, is also a b shell, by the way, you can also switch out the file system, right.Uh, and you can, and you can, and you can swap out the, the, the heartbeat for the, the crown framework, the, the loop that the agent framework itself. And so your agent basically is ba basically at the end of the day, it's just. It's just, its files. Um, and then, and then there's of course it a openswyx: call.Marc: Yeah, it's, it's basically, it's, it's just the files.Um, and then by the way, as a consequence of that, the agent and then the agent itself, it turns out a couple important things. So one is it, it's, it, it can migrate itself, right? And so you're, you can instruct your agent, migrate yourself to a different, uh, runtime environment, migrate yourself to a different file system, migrate yourself to a different, you know, swap out the language model.Your agent will do all that stuff for you. And then there's the final thing, which is just amazing, which is the agent is the agent actually has full introspection. It actually, it actually knows about its own files and it could rewrite its own files. Right. Which by the way, is basically no widely deployed software system in history where the, the, the thing that you're using actually has full introspective knowledge of how it itself works and is able to modify itself.Like that, that, I mean, there have been toy systems that have had that, but there, there's never been a widely deployed system that has that capability and then that leads you to the capability. That just like completely blew my mind when I wrap my head around it, which is you can tell the agent to add new functions and features to itself and it can do that.Extend yourself. Yeah. Right? Extend, extend yourself. Like extend yourself. Give yourself a new capability. Right? And so, and so literally it's just like you run into somebody at a party and they're like, oh, I have my open claw, do whatever, connect to my eat, sleep bed, and it gives me better advice and sleep.And you go home at night and you tell your claw, or if they're at the party, by the way, you tell your claw, oh, add this capability to yourself. And your claw will say, oh, okay, no problem. And it'll go out on the internet and it'll figure out whatever it needs and then it'll go out to claw code or whatever.It'll write whatever it needs. And then the next thing you know, it has this new capability. And so you don't even have to, like, you can have it upgrade itself without even having to, without having to do anything other than tell it that you want it to do that. And so anyway, so the, the combination of all this is just, I mean, this is just like a massive, incredible, I mean, it's just incredible.Like if I, if I were, if I were 18, like this is a hundred, this is what I would be spending all of my time on. This is like such an incredible conceptual breakthrough. Yeah. And again, pe people are gonna look at it and they already get this response. People are gonna look at it and they're gonna say, oh, well, where's the breakthrough?‘cause these, the, all of these components were already known before. Mm-hmm. But, but this is the key, the key to the breakthrough was by using all these components that were known before, you get all of the underlying capability of that's buried in there. And so all, and so for example, computer use all of a sudden just kind of falls, trivi, trivial.Of course it's gonna be able to use your computer. It has full access to the shell. Right. And then, and then you just, you, you give it access to a browser, and then you've got the computer and the browser and, and often away it goes. And, and then you've got all the abilities of the browser also. Um, yeah.And so, and so the capability unlock here is profound. My friends who are, you know, deepest into this, are having their claw do like a, like, literally like a thousand things in their lives. They have new ideas every day. They're just like constantly throwing new challenges at the thing. And by the way, it's early and, you know, these are, you know, these are prototypes and there are, you know, as you guys know, there's security issues.Yeah. And, and so, you know, there's a bunch of stuff to be ironed out, but the, the unlock of capability is just incredible.swyx: Yeah.Marc: And I, I have absolutely no doubt that everybody in the world is gonna, is gonna have at least, you know, an agent like this, if not an entire family of agents. And w

The Real Cost of Technology Dependence, FreeBSD 15 Linuxator with CUDA, Bidirectional OPNsense/pfSense, Netbase, a SYN attack, and more... NOTES This episode of BSDNow is brought to you by Tarsnap and the BSDNow Patreon Headlines The Real Cost of Technology Dependence: Building Independence with Open-Source Storage News Roundup Building Hierarchical Jails (Podman x Native Jail) on FreeBSD 15 FreeBSD 15.0 Linuxulator with CUDA Setup Bidirectional OPNsense/pfSense Firewall Configuration Migration/Conversion CLI SYN attack Syn attack follow up Netbase is Port of NetBSD Utilities to Another UNIX Like Operating Systems Beastie Bits OpenBSD -current moves to 7.9-beta - Delayed hibernation comes to OpenBSD/amd64 laptops Tarsnap This weeks episode of BSDNow was sponsored by our friends at Tarsnap, the only secure online backup you can trust your data to. Even paranoids need backups. Feedback/Questions Send questions, comments, show ideas/topics, or stories you want mentioned on the show to feedback@bsdnow.tv Join us and other BSD Fans in our BSD Now Telegram channel

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Jails for NetBSD, ARC and L2ARC sizing for Proxmox, Anatomy of bsd.rd, Docker Containers on FreeBSD, Running Time Machine inside a FreeBSD Jail, and more... NOTES This episode of BSDNow is brought to you by Tarsnap and the BSDNow Patreon Headlines Jails for NetBSD ARC and L2ARC Sizing on Proxmox News Roundup Lab: Anatomy of bsd.rd — No Reboot Required Exploring Docker containers on FreeBSD Time Machine inside a FreeBSD jail After decades on Linux, FreeBSD finally gave me a reason to switch operating systems Beastie Bits - - Tarsnap This weeks episode of BSDNow was sponsored by our friends at Tarsnap, the only secure online backup you can trust your data to. Even paranoids need backups. Feedback/Questions Emelio - openbsd Send questions, comments, show ideas/topics, or stories you want mentioned on the show to feedback@bsdnow.tv Join us and other BSD Fans in our BSD Now Telegram channel