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Version 7.6.1 released

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Picture of user Jan Wielemaker.

Version 7.6.1 has been released. Highlights:

  • Several race conditions in atom, functor and predicate `supervisor' installation have been fixed. Thanks to Keri, being a perfect detective! This work includes the installation of memory barriers, needed to avoid reordering memory access on e.g., ARM and powerpc.
  • Race condition in windows sockets. Keri.
  • Port: ARM: unsigned char issue in RDF Turtle parser. Added recognising ARM and powerpc in configure.ac to build by default as a shared object configuration.
  • Avoid unnecessary creation of modules for several built-ins.

Security Vulnerability in Pengines

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Picture of user Anne Ogborn.

This weekend Vladislav Zorov reported a vulnerability in pengine sandboxing.

This vulnerability allows any user who can create a pengine to execute unconstrained prolog code, including process_create.

The vulnerability can be triggered simply by loading pengines.

This vulnerability was patched with commit


We recommend all pengines users upgrade to this release or later.

Big thanks to Vladislav.

Playing with Prolog

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Picture of user Anne Ogborn.

Sam Neaves and Anne Ogborn have started a new YouTube channel featuring recreational uses of Prolog.

The first video shows a pengine powered Lego robot built by Sam. Watch it on YouTube

Fast cars and Prolog

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Picture of user Jan Wielemaker.

Now the dust has mostly settled for me, I thought people here might be interested in what we were using SWI-prolog for at Zerolight where I work. We wrote a configuration engine with it for really flash hypercars.

And I don't think I'd have been able to even remotely be finished on my part of the project now if it wasn't for Prolog and the support I got here.

Thanks :)

Steve (Stephen Coda)

SWI-Prolog Used For Satellite Anomaly Detection

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Picture of user Anne Ogborn.

Simularity, Inc. has announced a new SWI-Prolog driven Anomaly Detection technology.

SWI-Prolog has helped create a new technology.

It's easy enough to detect differences between two satellite images of the same area.

It's much harder to eliminate differences in the images caused by 'normal' factors such as different lighting, satellite position, and clouds.

And until now it's been impossible to automatically distinguish between unimportant changes like normal movement of vehicles and change of season, and important changes like new construction or the appearance of vehicles in areas where they didn't previously appear.

Now Simularity has developed Simularity AI-ADS, a system that can distinguish important changes from unimportant ones, using AI techniques.

Simularity recently partnered with Taqnia Services, the technology development company owned by the investment arm of the Saudi Arabian government, to provide imagery analysis services in Saudi Arabia.

Peter Koning, VP of Sales for Simularity,says "SWI-Prolog was crucial for our success. We couldn't have done this in any language but Prolog, and SWI-Prolog gave us the performance and library support we needed"