irony-install-server with MacPorts

The irony server provides symbol completion for irony-mode on Emacs. Under the hood, it uses libclang to parse C and C++ source. On Mac OS, the Xcode command line tools comes with clang, but it does not provide the necessary header files. Here are the specific instructions for MacPorts.

$ clang -v
Apple clang version 15.0.0 (clang-1500.3.9.4)
Target: x86_64-apple-darwin23.5.0
Thread model: posix
InstalledDir: /Library/Developer/CommandLineTools/usr/bin
$ sudo port install cmake clang-15
--->  Computing dependencies for cmake
...
--->  Cleaning cmake
--->  Computing dependencies for clang-15
...
--->  Cleaning clang-15
--->  Scanning binaries for linking errors
--->  No broken files found.                             
--->  No broken ports found.
$ cmake \
  -DLIBCLANG_LIBRARY=/opt/local/libexec/llvm-15/lib/libclang.dylib \
  -DLIBCLANG_INCLUDE_DIR=/opt/local/libexec/llvm-15/include \
  -DCMAKE_MACOSX_RPATH=1 \
  -DCMAKE_INSTALL_RPATH=/opt/local/libexec/llvm-15/lib \
  -DCMAKE_INSTALL_PREFIX=$HOME/.emacs.d/irony/ \
  $HOME/.emacs.d/elpa/irony-20190703.1732/server &&
cmake --build . --use-stderr --config Release --target install

Some notes:

• MacPorts llvm-15 package provides the libclang.dylib, but clang-15 provides the header files for it (see macports #69392).
• LIBCLANG_LIBRARY has to point to the dylib (or the .so on Linux), not the lib/ directory.

SSH server compromised by xz/liblzma 5.6.0 and 5.6.1

A backdoor compromising SSH server introduced in xz/liblzma 5.6.0 and 5.6.1 was reported today to oss-security by Andres Freund. According to the analysis, when the sshd binary is initialized by the dynamic linker at startup, the initialization code in liblzma installs a hook to the dynamic linker that modifies subsequent dynamic library symbol tables (before they are made read-only) that replaces SSH RSA encryption functions with malicious code.

Image credit: Wikimedia Commons

The xz repository provides a widely used data compression command line program “xz” as well as a library “liblzma” that allows the compression algorithm to be used in other programs. SSH is a remote secure login server. Although SSH does not use liblzma directly, many distributions such as Debian and Redhat patches it to integrate with systemd notification, which uses liblzma.

The malicious code in liblzma was introduced as obfuscated binary test data in a git commit and patched into compiled binary using an obfuscated M4 macro as part of the build system. The malicious git commit was introduced by JiaT75, who has been contributing xz commits for about two years while taking advantage of the mental health issues of the original author of xz, who maintained it as an unpaid hobby project. Most of Jia's commits are non-technical fixes such as translation or documentation. Jia reportedly urged Redhat and Debian maintainers of the xz package to push the new version to production, which suggests that it is premeditated. The attack was only discovered because Andres Freund noticed that his SSH login got slower and decided to investigate.

It is nearly impossible to manually audit supply chain attacks like this, but there is one way to mitigate this attack vector: all setuid binaries should be statically linked with the static-PIE linking option. Static linking eliminates the dynamic linking attack vector, while PIE enables address-space layout randomization (ASLR) to make it impossible for malicious actors to patch code in runtime. Allegedly, OpenBSD already compiles system binaries this way.

Some may have concerns about static linking, but they can read the refutation by Gavin D. Howard.

Advisory About Go

Static linking alone does not completely guard against runtime code patching, but we need address-space layout randomization (ASLR) for both code and data. That is because the data sections also contain function pointers that could alter the code path. Without ASLR for data, any function pointer in heap-allocated objects can be compromised by supply chain attack.

Go does not support heap data ASLR (golang/go#27583). This means that malicious code using the unsafe package could traverse the heap and override interface function pointers to change the behavior of existing code. This is despite the fact that Go language always compiles and statically links on the whole-program, and has a -buildmode=pie for code ASLR. Unfortunately, the Go crypto, ssh, tls, and net/http packages all make ample use of interfaces, and every one of these can be an attack vector.

Deep Dive into MQA-CD Encoding

A few weeks ago, I saw this video by Techmoan introducing the MQA-CD. MQA-CD is an audio CD that can be played back in a regular CD player, which is limited to 16-bit samples at 44.1 kHz. However, when played back through an MQA decoder, it promises better sound quality at 24-bit at 192 kHz.

Before we dig into the MQA marketing material, we need to understand that MQA is an encoding scheme that can exist outside of a CD, e.g. audio delivered over the radio or the Internet. Some of the non-CD transports are assumed to carry 24-bit at 48 kHz or higher. However, MQA-CD transport is limited to 16-bit at 44.1 kHz by the CD as its physical medium.

At first glance, MQA violates the Nyquist–Shannon sampling theorem which places a hard upper-bound that a signal at frequency B must be uniquely represented by at least 2B samples per second. However, we can give it some leeway by allowing for lossy encoding, even though some MQA marketing material claims that the encoding is lossless.

In a lossy scheme, we can steal some lowest significant bits from the sample to passthrough a data stream like MP3 that employs psychoacoustic coding. The lowest significant bits sound like the noise floor when listened to without the decoder, and the psychoacoustic coding allows us to put more detail into the noise more economically—basically, the data stream contains instructions about how to synthesize only sounds humans can hear, so we use less data than if we have to encode the full Nyquist-Shannon spectrum. Furthermore, the data stream only needs to contain the delta, which is the sound not already present in the non-stolen bits.

The question about MQA-CD is how many bits it is stealing?

Music Origami, according to MQA

The MQA website links to a blog by the MQA inventor, Bob Talks, which discusses the CD encoding with some technical detail, but it is a little confusing:

If the original source is 44.1kHz/24b or if the sample rate is 88.2, 176.4, 352,8 kHz, or DSD, then a standard MQA file will be 44.1 kHz/24b. The file contains the information for decoding, ‘unfolding’, and rendering.

This 24b MQA file is structured so that, if in distribution it encounters a ’16-bit bottle-neck’ (e.g. in a wireless or automotive application), then the information in the top 16 bits is arranged to maximise the downstream sound quality and still permits unfolding and rendering. See [2]

[2] MQA-CD: Origami and the Last Mile 

So reference [2] should contain some information about how the 24-bit is truncated to 16-bit. Here are some mentions:

The Green signal is completely removed by MQA decoders; but it is there so that we can hear more of the music when playback is limited to a 16-bit stream.

Sometimes we might want to listen to MQA music on equipment that doesn’t support 24 bits – maybe only 16? Rather than throw away all the buried information, MQA carries a small data channel (shown in Green) which can contain the ‘B’ estimates, enabling significantly improved playback quality on, e.g. a CD, over ‘Airplay’, in-car, to certain WiFi speakers and similar scenarios.

But it is also confusing because it shows the “Green signal” at -120 dB. We know that CD dynamic range is 96 dB, so it could not have been able to represent -120 dB noise floor. Samples at 24-bit has a dynamic range of 144 dB. However, the signal charts in the page shows a floor of -168 dB, and it was putting some information below -144 dB, which requires 28-bits.

As a side note, CD dynamic range of 96 dB is determined by the formula in terms of the 16-bit sample depth: \( 20 \times \log_{10}{2^{16}} \approx 96 \). As a rule of thumb, each bit in the sample represents about 6 dB in dynamic range.

Another page Deeper Look: MQA 16b and Provenance in the Last Mile also states that:

If we look at the block diagram above, we can see there are three components to the MQA data, broadly described as: i) top 16 bits, ii) MQA signalling and iii) bottom 8 bits

The block diagram clearly shows that the encoding result in 24-bit master file, but it still does not explain how that is reduced to MQA-CD which is bottlenecked to 16-bit samples.

Is Bit Stealing Plausible?

Since MQA still does not explain how the 24-bit master is reduced to 16-bit transport depth on a CD, we are left to speculate about the bit stealing idea earlier.

If we allow stealing 4 bits per sample, then we get a data rate of \( 2 \textit{ channels} \times 4 \textit{ bits per sample} \times 44100 \textit{ Hz} \approx 344 \textit{ kbps} \). This is pretty generous for high quality AAC, which is typically 256 kbps. The dynamic range before decoding is reduced from 96 dB to 72 dB, which is still comparable to a very high quality magnetic tape.

So I would say it is plausible, but it is inconclusive from the MQA marketing material if this is how they did it.

Furthermore, I don’t see the point of MQA’s “Music Origami” that folds 24-bit 192 kHz into 24-bit 48 kHz. If the transport is already capable of lossless 24-bit data, it must be a digital transport that is not a CD, which means there is no requirement to maintain backwards compatibility with a Red Book CD player. We can just use the whole stream to transport encoded audio, e.g. AAC or Flac. Even some later CD players in the 2000’s can play MP3 from a data CD or from a USB drive. That was all possible before MQA launched in 2014.

Which is why Techmoan says that even if you believe MQA delivers higher quality audio, it is a format that came a little too late.

Carrot and Stick Security Design

Carrot and stick security design is the idea to have frontend and backend work together to enforce security policies in software. The frontend interacts with the user and steers them towards compliance, while the backend enforces the security rules. Although we don’t necessarily use carrot and stick to mean reward and punishment, the carrot is a “soft nudge” and the stick is a “hard boundary.” If the user bypasses the frontend and tries to interact with the backend directly, they will be met with a hard error message.

Image credit: Wikimedia Commons

(“Good cop, bad cop” is a similar strategy, although the cop analogy may be controversial.)

An example is a photo gallery that allows visitors to browse but only signed-in users to download images. The frontend may present a “download” button but will ask the user to either login or create an account. The backend checks that the login credentials are present before allowing the image to be downloaded.

If the security check is only done in the frontend, then the user could simply bypass login by forging a URL request directly to download the images. If the security check is only done in the backend, then an innocent user that did not know they need to signup or login first may be confronted with an unfriendly error message.

I’m intentionally using the terms “frontend” and “backend” loosely. In practice, the designations may differ depending on the application:

• For a user-facing website, frontend is the client side Javascript, and backend is the HTTP server.
• For a mobile app, frontend is the app, and backend is some API used by the app.
• For an API, frontend is the HTTP server middleware, and backend is the internal data storage.

Even at the API level, the API design should try to encourage well-defined use cases (the carrot), and let the protocol layer check for malformed requests (the stick).

What this means is that a complete software stack that spans the gamut of client side Javascript or app, an API middleware, and backend storage should implement security enforcement at all layers.

Memory Safety State of the Union 2024, Rationale Explained

There has been renewed interest in programming languages after The White House recently published a recommendation suggesting the transition to a memory safe language as a national security objective. Although I am not an author of the report, I want to explain the rationale that someone might use to consider whether their infrastructure meets the memory safety recommendations. This is more of an executive-level overview than a technical guide to programmers.

Image credit: Wikimedia Commons, Whitehouse North.

On February 26, 2024, the White House released Statements of Support for Software Measurability and Memory Safety calling attention to a technical report from the Office of the National Cyber Director titled “Back to the Building Blocks: A Path Towards Secure and Measurable Software” (PDF link). The whole framework works like this: we ultimately want to be able to measure how good the software is (e.g. by giving it a score), and it relies on memory safety as a signal. The tech report also references another report published by Cybersecurity and Infrastructure Security Agency (CISA) titled The Case for Memory Safe Roadmaps which contains a list of memory safe language recommendations.

To supplement their list, I will be using the TIOBE index top 20 most popular programming languages to provide the examples: Python, C, C++, Java, C#, JavaScript, SQL, Go, Visual Basic, PHP, Fortran, Pascal (Delphi), MATLAB, assembly language, Scratch, Swift, Kotlin, Rust, COBOL, Ruby.

I will also throw in some of my personal favorites: LISP, Objective Caml, Haskell, Shell, Awk, Perl, Lua, and ATS-lang.

High Level Languages

Languages that do not expose memory access to the programmer tend to be memory safe. The reason is that it reduces the opportunity for programmers to make memory violation mistakes. When accessing a null pointer or an array with an out of bounds index, these languages raise an exception that could be caught, or return a sentinel value (0 or undefined value), rather than silently corrupting memory content.

Examples: Python, Java, C#, JavaScript, SQL, Go, Visual Basic, PHP, MATLAB, Scratch, Kotlin, Ruby; LISP, Objective CAML, Haskell, Shell, Perl, Awk, Lua.

Under the hood, they employ an automatic memory management strategy such as reference counting or garbage collection, but programmers will have little to no influence over it because the language does not expose memory access.

It does not matter whether the language execution happens at the abstract syntax tree (AST) level, compiled to a byte code, or compiled to machine code. In general, any language could have a runtime implementation that spans the whole spectrum through Just In Time compilation.

Things to Watch Out For

High level languages are prone to unsanitized data execution error such as SQL injection, Code Injection, and most recently Log4j. This happens when user input is passed through to a privileged execution environment and treated as executable code. High level languages often blur the line between data and code, so extra care must be taken to separate data from code execution. Data validation helps, but ultimately data should not have influence over code behavior unless it is explicitly designed to do so.

I strongly oppose using PHP or any products written in PHP, which is particularly notorious for SQL and code injection problems and single handedly responsible for all highly critical WordPress vulnerabilities. But if you inherited legacy infrastructure in PHP, there are principles that will help hardening it.

Even though memory access errors are raised as an exception, if these exceptions are not caught, they could still cause the entire program to abort. They also still allow potentially unbounded memory consumption leading to exhaustion, which causes program to abort or suffer severely degraded performance, leading to denial of service.

Some languages provide an “unsafe” module which is essentially a backdoor to memory access. Using them is inherently unsafe.

• Python: ctypes — A foreign function library for Python
• Java: The Unsafe Class: Unsafe at Any Speed
• C#: Unsafe code, pointer types, and function pointers
• Go: unsafe package

Most languages also allow binding with an unsafe language through a Foreign Function Interface (ffi) like SWIG. This allows the high level code to run potentially unsafe code written in a non-safe language like C or C++.

Mid Level Languages

These languages expose some aspects of memory management to the programmer—such as explicit reference counting—and provides language facilities to make it safer.

Examples: Swift, Rust; also ATS-lang.

Performance is the main reason to use these languages, as memory management overheads have negative performance impact, and in some time-sensitive applications, we have to carefully control when to incur these overheads. The tradeoff is programmer productivity, since they have to worry about more things. Since performance is the main concern, these languages tend to be compiled into machine code before running in production.

I want to call out ATS-lang because it is a language I helped working on for my Ph.D. advisor, Hongwei Xi. It was conceived in 2002 and predated Rust (2015). ATS code can mostly be written like Standard ML or Objective CAML with fully automatic memory management. It also provides facilities to do manual memory management. Safety is ensured by requiring programmers to write theorems to prove that the code uses the memory in a safe manner (papers). The theorem checker uses stateful views inspired by linear logic to reason about acquisition, transferring of ownership, and disposal of resources.

Things to Watch Out For

These languages are safe by virtue that the compiler can check for most programmer errors in compile time, but these languages still provided unsafe ways to access memory.

• Swift: UnsafePointer
• Rust: Unsafe Rust
• ATS-lang: Unsafe C-style Programming in ATS

Furthermore, they are still prone to denial of service, SQL injection, and code injection vulnerabilities.

Low Level Languages

These languages require the programmer to manually handle all aspects of memory management for legacy reasons. For this reason, they are inherently unsafe.

Examples: C, C++, Pascal.

Although garbage collection was invented by John McCarthy in 1959 for the LISP language, that concept did not gain mainstream adoption until much later.

Even so, there are a few strategies to make these languages more memory friendly.

1. Use an add-on garbage collector like Boehm-GC. Note that object references stored in the system malloc heap are not traced for liveness, so care must be taken when using both GC malloc and system malloc.
2. C++ code should use Resource Acquisition is Initialization (RAII) idiom as much as possible. The language already tracks object lifetime through variable scope. The constructor is called when an object is introduced into the scope, and the destructor is called when the object leaves the scope. Smart pointers like std::unique_ptr and std::shared_ptr use RAII to manage memory automatically.

My particular contribution in this field is my Ph.D. dissertation (2014), which proposed a different type of smart pointer in C++ that does not auto-free memory but still helps catching memory errors. I showed that it is practical to use, by implementing a memory allocator using the proposed smart pointer.

Legacy Languages

I have omitted Fortran and COBOL from any of the lists above. Historically, Fortran and COBOL only allowed static memory management. All the memory to be used by the program are declared in the code, and the OS provisions for them before the program is loaded for execution. However, they never had any array bounds checking, so they are not memory safe. Furthermore, attempts to modernize the language with dynamic memory allocation exacerbated the problem that these languages were not designed to be memory safe.

• Fortran: memory management
• COBOL: memory allocation

I have also omitted assembly languages as a whole. Assembly languages tend to be bare metal and provide completely unfettered memory access, but there have been some research to enhance the safety of assembly languages (e.g. Typed Assembly Language).

Conclusion

There is no language that is completely memory safe. Some languages are safer by design because either it limits the ability of the programmer to manipulate memory, or it gives the programmer facilities to help them use memory in a safer way. However, almost all languages have ways to unsafely access memory in practice.

Memory safety is also not the only way to cause security breach. Executing data as code is the main reason behind SQL and code injection, and these lead to highly critical privilege escalation, penetration, and data leak attacks. One safety net we can provide is by making code read-only, but this does not absolve the importance of good data hygiene.

Unbounded memory consumption can degrade performance or cause denial of service attacks, so memory usage planning must be considered in the infrastructure design.

In any case, the language is not the panacea to the memory safety or security problems. Programmer training and a culture to emphasize good engineering principles are paramount. However, I would love to see a renewed interest in programming language research to enhance language safety, by designing languages that encourage good practices.

Further Resources

• Memory management in various languages

Infinite Monkey Theorem, Debunked

If you let a monkey hit random keys on a typewriter for an infinite amount of time, will it happen to write the complete works of Shakespeare? Common wisdom says that not only will it write Shakespeare, but it will “almost surely” type every possible text an infinite number of times.

The proof idea goes like this: the probability to produce a permutation matching the complete works of Shakespeare is very small, but still greater than zero, so it will “almost surely” happen.

This is assuming that the monkey will eventually produce such permutation with a non-zero probability.

What if some permutations are simply not possible? There could be several reasons why this particular monkey and this particular typewriter could not have written Shakespeare. In order of increasing complexity:

• Some letters on the keyboard are broken, so some letters in the works of Shakespeare could not be typed.
• Due to the clumsiness of the monkey, it is only able to hit the space bar before or after hitting the letters above it. This means that every word has to always begin and end with ‘z’, ‘x’, ‘c’, ‘v’, ‘b’ ‘n’, or ‘m’. Shakespeare often used words that do not satisfy this criteria, so the works of Shakespeare could not have been written.
• The monkey is not able to repeat what it types within a certain number of words, so phrases like “to be or not to be” can never be written.

Infinity does not mean that it will allow every permutation. An example is the infinite sequence that concatenates the set of numbers consisting of only even digits: 0 2 4 6 8 20 22 24 26 28 40 42 44 46 48 etc. This sequence is infinite, but it would never contain the number ‘1’.

(On the other hand, the sequence of all even numbers concatenated will contain all finite numbers, including the odd numbers; since starting from 10—which is still an even number—we simply ignore the last digit and look at the tens and above, e.g. 123 is contained in 1230 1232 1234...)

This is not an argument about “almost surely” being practically zero. Instead, we are simply saying that infinite monkey is not almost surely, as the output is handicapped in some way albeit still infinite.

If we want to apply the infinite monkey theorem elsewhere, infinity alone is not sufficient. We have to also show that the desired permutation has a non-zero probability to happen, as this is not a given. On the contrary, an application that only establishes infinity can be disproven by showing that the desired output actually has a zero probability of happening.

∞ ⨉ 𝜀 = ∞ but ∞ ⨉ 0 = 0

In reality, we often confuse the mechanism of the permutation with the existence of output. We cannot say that the complete works of Shakespeare exists in the output implies that it must have been produced by the monkey. What if we have a programmed typewriter such that, if the monkey hits ‘s’ a million times in a row, would insert the complete works of Shakespeare? The complete works “exists” in the output, but it is not produced through the monkey's mechanism of permutation.

(This is analogous to the panspermia hypothesis saying that life could have been introduced from an external source rather than permuted naturally within the system. The hypothesis allows that a system capable of sustaining finite life may not have been able to produce life infinitely.)

Showing that some improbable event has zero probability of being produced could be quite difficult but “almost surely” doable. However, if we try to categorically argue that “almost surely” is practically zero, then the argument is only “almost surely” believable.

Deep Learning and Solving NP-Complete Problems

This recent article about AlphaGeometry being able to solve Olympiad geometry proof problems brings to my attention that there have been active research in recent years to combine deep learning with theorem proving. The novelty of AlphaGeometry is that it specializes in constructing geometry proofs, using synthetic training data guided by the theorem prover, which previous approaches have had difficulty with.

As a formal method like type checking of programming languages, theorem proving tries to overcome the ambiguity of mathematical proofs written in a natural language by formalizing proof terms using an algebraic language with rigorous semantics (e.g. Lean), so that proofs written in the language can be verified algorithmically for correctness. In the past, proofs had to be tediously written by humans, as computers had great difficulty constructing the proofs. Computers can do exhaustive proof search, but the search space blows up exponentially on the number of terms, which renders the exhaustive search intractable. However, once the proof is constructed, verification is quick. Problems that are exponentially intractable to solve but easy to verify are called NP-complete.

Traditional proof searching limits the search space to simple deductions, which can offload some tedium from manually writing the proofs. In recent years, machine learning is used to prune or prioritize the search space, making it possible to write longer proofs and solve larger problems. It is now possible for computers to construct complete mathematical proofs for a selected number of high school level math. The idea is a natural extension to what AlphaGo does to prune the game's search space, which I have written about before.

This is quite promising for a programming language that embeds theorem proving in its type system to express more elaborate safety properties, such as Applied Type System (ATS) designed by my advisor, which I have written about before. Programs written in ATS enjoy a high degree of safety: think of it as a super-charged Rust, even though ATS predates it. Now that generative AI can help people write Rust, a logical next step would be to create a generative AI for ATS.

I would be interested to see how machine learning can be applied to solve other NP-complete problems in general, such as bin-packing, job shop scheduling, or the traveling salesman problem. However, machine learning will face fierce competition here, as these problems are traditionally solved using approximation algorithms, which comes up with a close-enough solution using fast heuristics. Also, only a few of them have real-world applications. Admittedly, none are as eye catching as "AI is able to solve math problems" giving the impression of achieving general intelligence; in reality, the general intelligence is built into the theorem proving engine when it is used in conjunction with machine learning.

However, I think proof construction is a very prolific direction that machine learning is heading. Traditionally, machine learning models are a black box: even though it can "do stuff," it could not rationalize how to do it, let alone teaching a human or sharing the same insight with a different machine learning model. With proof construction, at least the proofs are human readable and have a universal meaning, which means we can actually learn something from the new insights gained by machine learning. It is still not general intelligence, and the symbolic insight to be gained might very well be a mere probabilistic coincidence, but it does allow machine learning to begin contributing to human knowledge.

So many camera systems! My thoughts.

My camera journey is a little meandering, but it can roughly be roughly summarized as "all I want is the perfect camera."

My first camera is a Canon PowerShot A50, bought in 1999, which is a compact CCD camera. It can do 10-bit RAW, which I later discovered allowed me to correct underexposed photos more easily, and the colors actually looked decent! The camera writes to an original CompactFlash. Without a reader, you'd have to transfer images out of the camera with a serial port dongle which is very slow (115200 baud, or ~14 kbps). I later sold it at a fundraiser.

Next I had a Sanyo Xacti VPC-HD1000, bought in 2007, which is a camcorder that can shoot 720p60, 1080p30, or 1080i60 video. It also came with a strobe light for photos. It had some very rudimentary electronic stabilization that didn't work very well. I tried Jonny Lee's Poor Man Steadicam hack, but I was still not happy with the result. After a few years of use, the sensor suddenly went grey on me, and eventually it was disposed through electronics recycling.

My next camcorder is a Panasonic HC-V750, bought in 2014, which can do 1080p. It had great image stabilization out of the box, but the color was a little washed out, so I never really used it very much.

My first somewhat serious camera is the Lytro Illum. Bought in 2015, it was the first computational camera that will let you adjust focus and depth of field after a picture has been taken. It was ahead of its time. Even though it had a 40MP sensor, the pixels had to record depth, so the final image was only about 10MP, and it was very noisy in low light. Nonetheless, it was a great tool to study depth of field in composition. I still have it, but after Google acquired Lytro, they discontinued support. The software still works on an Intel Mac, and it should work on the Apple ARM silicon for now using Rosetta 2 emulation, but its years are numbered. The Lytro software on Windows may have a better luck!

In 2018, I decided I want an interchangeable lens camera that can shoot video. At that time, the Panasonic GH5S was one of the first that can shoot 4K 30p 10-bit in camera, although the 10-bit video is not natively supported by Mac OS even til this day (VLC/FFmpeg supports it). In the next few years, I also started a collection of Micro Four-Thirds lenses. I will talk about them later.

I was pretty happy with the GH5S, except the contrast detection autofocus is not very good. The lack of IBIS also meant I could not do hand-held video very much. Some lenses have OIS, but they are not quite enough. I bought a gimbal, but it attracted enough attention that I was asked to leave the premise a few times when out shooting. Also, since Mac OS does not natively support the 10-bit H.264 from the GH5S, I have to transcode them to ProRes when editing the videos, which is a pain. As a result, I used it more for stills photography. The 14-bit RAW is great for color grading, though the sensor is limited to 10.2 MP.

The 14-bit RAW at 10.2 MP means the GH5S is excellent for taking time lapses. The camera's built-in conversion only resulted in a lower-resolution 8-bit video, which I ended up using more due to laziness, but the RAW files can be converted to DNG using Adobe DNG Converter and edited in Davinci Resolve for the most pristine quality time lapse.

In 2022, I bought two more cameras which overcompensated for the want of image stabilization and the want of megapixels.

The camera that overcompensated for my want of image stabilization is the GoPro Hero 10. Although the image stabilization is decent, the 4K 60p video is very noisy even during daytime in the shade. The 5.3K cinematic video is probably better, but it can still only record in 8-bit video (their next release later in 2022, Hero 11, records in 10-bit). The GoPro also tends to overheat after 20 minutes or so of continuous recording. This is my first camera that I have to worry about overheating, and sadly isn't the last camera. As a result, I did not end up using it very much.

The other camera that overcompensated for my want of megapixels is the Fujifilm GFX 100S, which is a whopping 102 megapixels. It is very satisfying to keep enlarging the picture and play a game of "where's waldo" with all the details. However, when pixel peeing, I can see some issues with chromatic aberration and corner softness at F/4 with the 32-64mm zoom lens. I don't have a different lens to know if this is a common issue. I've seen some photographers stopping down to F/22 which tends to make the lens sharper for the high resolution.

Like its predecessor GFX 100, the 100S can shoot 4Kp30 10-bit videos but is more compact. The phase-detect autofocus works great for me. The image stabilization is excellent both for photo and video, although in some situation the video can momentarily have a wobbling effect. Above all, I am most impressed with the colors. After experimenting with a few settings, combining the DR-P (dynamic range priority) mode with Velvia film simulation gave me the best result. The DR-P on its own would give a color that is too flat, but the Velvia simulation restored some contrast; Velvia simulation on its own tends to be too contrasty, since originally it is a color reversal film that only had 5 stops of dynamic range. Using DR-P and Velvia together balanced out the colors very well.

Although I shoot in RAW+JPEG, the out-of-camera colors are so good that my attempt to color grade the RAW usually results in slightly worse colors! The film simulation can also be applied to the 10-bit videos, and I am very impressed with the video colors as well. The video is 10-bit in H.265, which is natively supported by Mac OS. The 16-bit RAW from GFX 100S are still about 100MB a piece after lossless compression, so I find myself really slowing down and be more meticulous with the composition when taking the photos. However, it still attracted unwanted attention. I was also asked to leave the premise a few times when shooting.

Towards the end of 2023, I bought two more cameras that overcompensated for the avoidance of unwanted attention.

The first was the Sony A6700. It is slightly larger than the ZV-E1, but the A6700 in APS-C has more compact lenses than the ZV-E1 which is full-frame. The video looks very clean at 4K 60p. The low-light performance is great. Clear image zoom (digital zoom) also looks excellent. Not to mention, Sony has the best autofocus. However, the image stabilization of Sony is lacking: the footage is usable when standing still, but unusable when walking, which means I would have to use the gimbal again. It also tends to overheat after 20 minutes, but the Ulanzi external fan helped.

For the second camera, I decided to try the DJI Osmo Pocket 3. I am very impressed with the low light performance. At night, its default setting tends to overexpose the image, but I find that -2 EV compensation looked the best. The 4K has a lot of details at no zoom, but will begin to lose detail with digital zoom. Autofocus is reasonable, although I do notice the occasional hunting. The image stabilization is also impeccable being a gimbal itself, and it fits into the palm of my hand! I think this has the best potential for avoiding unwanted attention.

In reality, I probably will have to use the Pocket 3 and A6700 interchangeably: shoot with Pocket 3 when walking, and A6700 with the 16-50mm lens if I need to zoom.

My most horror with the A6700 happened when a friend asked me to do a photoshoot for their family, and they insisted that I use "my latest camera." After being spoiled with Fujifilm, I was unimpressed with Sony's S-Cinetone which is supposed to be their best color profile. However, when color grading the RAW files, I have to start from the terrible lens distortion and vignetting from the pancake lens I used. Remember I bought this camera for the compactness, not for the glass! Correcting the lens defects in post is doable, which Sony can also do in camera very well, but it took me hours. This is probably why the Sony lets me apply my own LUTs in camera, and many photographers sell their LUTs online.

So my experience with cameras is indeed quite meandering, but I will summarize my thoughts on camera systems here.

Canon

I purposefully avoided Canon throughout the years. I will give them credit that they have a respectable camera system with a wide selection of EF mount lenses for DSLR and now RF mount lenses for mirrorless cameras. Also respectably, Canon still develops their own sensors with reasonably good colors, while most other cameras just use Sony sensors. However, I avoided Canon for two reasons:

• Back in the days, they have deliberately crippled the video capabilities of their photography cameras to avoid cannibalizing on their cinematic camera line. This changed with the Canon EOS R5 which can shoot 8K internally in RAW. Although R5 has overheating issues, the R5C addressed it with an internal fan.
• Although there are many third-party EF lenses and third-party cameras supporting the EF mount, they are reverse-engineered and not officially supported by Canon. Furthermore, Canon started being litigious with third-party RF lenses.

This means that photographers are buying into the Canon walled-garden and have to put up with their superficial product segmenting which could come again in the future if not now.

Nikon

I really don't have much experience with Nikon. I know some people are happy with it. That's about it.

Micro Four-Thirds

I have the most experience with Micro Four-Thirds, which includes cameras and lenses made by Panasonic and OM Systems (was Olympus). Panasonic lenses are pretty good for video, but I like the Olympus lenses best for their superior optical quality and bokeh. I am absolutely in love with the clutch manual focus of Olympus lenses: clutching the focus ring will engage in mechanical manual focus, and de-clutching it will allow autofocus as well as focus by wire. It gives you a taste of pulling manual focus like a cine lens, but Olympus lenses only have 90° of focus pull rotation compared to the 200° for cine lenses, which makes it a little too sensitive for cinematography.

Micro Four-Thirds sensor size is 1/4 the surface area of full-frame with exactly 2x crop factor, so focal length conversion is pretty straightforward. Due to the smaller sensor, the native telephoto lenses are miniature compared to the full-frame counterparts. The sensor size works well with Super35 (APS-C) cinema lenses. It is also possible to use the camera with B4 ENG lenses made for 2/3" sensor with the 2x teleconverter. In theory, this makes this camera system the most versatile and diverse, but throughout the years it had a lot of unrealized potential.

First is that the cameras were late to the phase-detect autofocus. Olympus had PDAF first, but their cameras could not shoot 10-bit video. Panasonic only recently released G9 II with PDAF. Even so, the G9 II body is exactly the same size as the full-frame S5 IIx. I feel that Micro Four-Thirds makes more sense with compact cameras and pancake lenses, but so far Sony is eating their lunch on compactness.

L-Mount

Another camera system of interest is the L-Mount, which is originally designed by Leica but now includes Panasonic, Sigma and DJI in a closed consortium. It has great lens selection in full-frame and APS-C format, and the APS-C system can potentially be very compact as well. Leica cameras and lenses are premium, but Panasonic also has great cameras and lenses for video.

It is a promising system with good vendor diversity.

Sony E-Mount

Sony makes the vast majority of camera sensors, so there is no question about the image quality there. I am just not personally a fan of their color science.

Sony is also reasonable when it comes to licensing E-Mount to other lens makers, so there is a great diversity of lenses from Sigma, Voigtlander, and even Fujinon. However, the E-Mount is fundamentally designed for APS-C sensor size and not for full-frame, as the throat-diameter partially occludes the corners of a full-frame sensor. This can become a challenge in lens design where the distortion and vignetting will have to be corrected in camera or in post. Lens distortion and vignetting is prominent even in their high-end FE G-master lenses (e.g. FE 20-70mm F/4 G, FE 20mm F/1.8 G).

This means photographers who want to work with RAW will find themselves spending more time in post-processing. They could save some time if they save their own LUTs to the camera and let the camera do both lens and color correction, which explains why Sony cameras seem to have a thriving after-market of LUTs.

Sony makes almost all E-Mount cameras, but it is not easy to choose the right camera, as they have many lines of camera segmented for every need and every price point. This is as far as I could gather about their camera lineup, as of 2023.

Purpose	Full Frame	APS-C
Cinema	FX3, FX6, FX9	FX30
Vlogging	ZV-E1	ZV-E10
Photography	A7R V, A1	A6700
Videography	A7C II, A9 III	n/a

Some of the cameras share the same sensor, for example FX3 and ZV-E1 share the same full-frame sensor, and FX30 and A6700 share the same APS-C sensor. Apart from the obvious form-factor difference, newer cameras tend to have more computational features that are missing from older cameras. Sony is known for neglecting their firmware updates.

The A6700 is a more recent camera that is supposed to have the most intuitive user interface, but I still find myself confused. For example, if I shoot RAW photos, it also disables Clear Image Zoom for video. H.265 only has 60p or 24p, but 30p is missing. Sony also does not use the common names for video codecs: H.264 is called XAVC S, and H.265 is called XAVC HS. The camera has three separate modes: photo, video, and S&Q (slow and quick), but the custom presets in these modes are all completely independent. In photo mode, the video button can still record video, but in video mode the shutter button is disabled. None of it makes sense!

As far as I can tell, all the non-cinema cameras can overheat when shooting long video, and that is probably by design. Sony cameras tend to be the most compact, which makes thermal management more challenging.

Fujifilm / Fujinon

Fujifilm is the camera brand, while Fujinon is the lens brand. Fuji has two separate systems: XF mount for APS-C, and G mount for medium format. Sigma and Tokina also make third-party XF lenses. Venus Laowa also makes some G mount lenses. However, the vast majority of XF and G lenses are made by Fujinon, so the selection is more limited, though you should be able to find a lens for most focal length and aperture.

What Fujifilm is famous for is its legendary film simulation in camera, as I have mentioned before. The film simulation profiles are based on color analysis of real film stock: Astia, Provia, Velvia, Eterna, Acros, etc. I think they are successful not so much because of the nostalgic factor, but because the colors from the original films were already time-proven.

They recently released GFX 100 II which can shoot 8K, but due to the computational requirement to scale 100 MP down to 8K, the 8K is cropped from the 35mm image area which is a shame. I actually think that a GFX 40 MP 8K makes a lot of sense. They already have X-H2 which is 40MP in APS-C that can shoot 8K non-cropped. I would be happy buying into their XF system in the future.

Action Cameras, etc.

Action cameras like the GoPro can be fun to use for sports and travel and fairly easy to carry around as an extra camera just in case or for behind-the-scenes footage. They are more durable than camera phones and can stand a lot of abuse. However, the GoPro market share is increasingly getting eroded by Insta360, DJI, and other Chinese companies. Action cameras are also known to have poor low-light performance. The DJI Osmo Pocket 3 is probably going to eat their lunch, except it is not weather-proof or water-proof. People who use action cameras will probably always juggle multiple cameras anyways.

Conclusion

If you ask for my recommendation, my favorite camera system by far is still the Fujifilm for the legendary color science alone. It is such a joy to shoot.

I still think that the Micro Four-Thirds has a lot of potential. I would not mind replacing my GH5S with the G9 II when it breaks, but right now I do not see a need to upgrade. I would like to see more compact cameras and pancake lenses.

Sony is probably fine for casual video, but I don't recommend it for photographers because of the time they would need to spend in post-processing to correct the lens distortion and colors. Sony cameras are also not as intuitive to use, but I suppose one can get used to it.

Although my opinion about the Sony camera system is not favorable, this is just speaking from my own experience with some influence from the research done by others. In the end, photography is highly subjective to personal preference.