diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml
index 0eb81e43cd..8a583187e2 100644
--- a/.github/workflows/deploy.yml
+++ b/.github/workflows/deploy.yml
@@ -47,7 +47,7 @@ jobs:
run: julia --project -e '
using Pkg; Pkg.instantiate();
using NodeJS; run(`$(npm_cmd()) install highlight.js`);
- using Franklin; optimize();
+ using Franklin; optimize(prerender=false);
cp(joinpath("__site", "feed.xml"), joinpath("__site", "index.xml"))'
- name: Deploy (preview)
if: github.event_name == 'pull_request' && github.repository == github.event.pull_request.head.repo.full_name # if this build is a PR build and the PR is NOT from a fork
diff --git a/_assets/blog/2021-1.6-highlights/julia-1.0-stacktrace.png b/_assets/blog/2021-1.6-highlights/julia-1.0-stacktrace.png
new file mode 100644
index 0000000000..02212be97e
Binary files /dev/null and b/_assets/blog/2021-1.6-highlights/julia-1.0-stacktrace.png differ
diff --git a/_assets/blog/2021-1.6-highlights/julia-1.6-stacktrace.png b/_assets/blog/2021-1.6-highlights/julia-1.6-stacktrace.png
new file mode 100644
index 0000000000..5e869912e7
Binary files /dev/null and b/_assets/blog/2021-1.6-highlights/julia-1.6-stacktrace.png differ
diff --git a/_css/franklin.css b/_css/franklin.css
index 70c1825a2b..e54e7a4413 100644
--- a/_css/franklin.css
+++ b/_css/franklin.css
@@ -71,3 +71,15 @@
background-color: #d8ffd8;
border-color: #404040;
}
+
+/* Specific colours for code highlighting */
+
+.hljs-metaj, .hljs-metas, .hljs-metap { font-weight: bold; }
+
+.hljs-meta { color: #b14e8f; } /* @... */
+.hljs-metaj { color: rgb(25, 179, 51); } /* julia> */
+.hljs-metas { color: red; } /* shell> */
+.hljs-metap { color: rgb(51, 131, 231); } /* pkg> */
+
+.hljs-string {color: #ca4418;}
+.hljs-subst { color: inherit;}
diff --git a/_layout/head.html b/_layout/head.html
index 03b31f5683..b13b1767ad 100644
--- a/_layout/head.html
+++ b/_layout/head.html
@@ -3,13 +3,15 @@
{{insert meta.html}}
- {{insert head_scripts.html}}
{{if hasmath}} {{insert head_katex.html }} {{end}}
{{if hascode}} {{insert head_highlight.html }} {{end}}
+
+ {{insert head_scripts.html}}
+
{{isdef title}} {{fill title}} {{end}}
{{ispage /blog/* /jsoc/gsoc/*}}
diff --git a/_libs/highlight/highlight.pack.js b/_libs/highlight/highlight.pack.js
index 4f6138fd3a..8f0f27ec0d 100644
--- a/_libs/highlight/highlight.pack.js
+++ b/_libs/highlight/highlight.pack.js
@@ -1,2 +1,2 @@
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diff --git a/blog/2021/03/julia-1.6-highlights.md b/blog/2021/03/julia-1.6-highlights.md
new file mode 100644
index 0000000000..0b2578719c
--- /dev/null
+++ b/blog/2021/03/julia-1.6-highlights.md
@@ -0,0 +1,451 @@
+@def title = "Julia 1.6 Highlights"
+@def authors = "Jeff Bezanson, Ian Butterworth, Nathan Daly, Keno Fischer, Jameson Nash, Tim Holy, Elliot Saba, Mosè Giordano, Stefan Karpinski, Kristoffer Carlsson"
+@def published = "24 March 2021"
+@def rss_pubdate = Date(2021, 3, 24)
+@def rss = """Some highlights of the Julia 1.6 release."""
+
+Julia version 1.6 has been released. Most Julia releases are timed and hence not
+planned around specific features, but this release was an exception since it is
+likely to become the next long-term support (LTS) release of Julia. Because of
+this, we took extra time developing the release to make sure that features which
+are needed for the future health of the ecosystem made it into the release.
+Also, the release was tested for regressions against all registered open source packages
+and issues were tracked down and fixed. The
+final decision about whether Julia 1.6 will become the new LTS will be made
+after it has been battle-tested in the field, around the time of the 1.7 release enters stabilization.
+The full list of changes can be found in
+the [NEWS file](https://github.com/JuliaLang/julia/blob/release-1.6/NEWS.md),
+but here we'll give a more in-depth overview of some of the release highlights.
+
+\toc
+
+## Parallel precompilation
+
+_Ian Butterworth_
+
+Executing all of the statements in a module often involves compiling a large
+amount of code, so Julia creates precompiled caches of the module to reduce this
+time. In 1.6, this package precompilation is faster and happens before you leave
+`pkg>` mode. Until now, precompilation took place solely as a single-processed
+sequence, precompiling dependencies one-by-one when needed during the linear
+code loading process when a package is `using`/`import`-ed for the first time.
+
+The olden days of <= 1.5, taking as an example `DifferentialEquations`; a popular
+package with a particularly high number of dependencies:
+```julia-repl
+(v1.5) pkg> add DifferentialEquations
+...
+julia> @time using DifferentialEquations
+[ Info: Precompiling DifferentialEquations [0c46a032-eb83-5123-abaf-570d42b7fbaa]
+ 474.288251 seconds …
+```
+In 1.6, `pkg>` mode gains a heavily parallelized precompile operation that is
+auto-invoked after package actions, to keep the active environment ready to
+load.
+```julia-repl
+(v1.6) pkg> add DifferentialEquations
+...
+Precompiling project...
+ Progress [========================================>] 112/112
+112 dependencies successfully precompiled in 72 seconds
+
+julia> @time using DifferentialEquations
+ 4.995477 seconds …
+```
+Whereas the previous code loading precompilation process took ~8 minutes to
+precompile and load `DifferentialEquations` without indicating progress while doing so,
+the new mechanism took a little over 1 minute to precompile while showing progress
+through the dependencies. Then the first time the package is loaded, it loads at full
+speed. The new parallel precompilation process takes a depth-first approach of
+working through the dependency tree in the manifest, first precompiling the
+packages with no dependencies and working upward to the packages listed in the
+environment’s `Project.toml`, allowing multiple packages to be precompiled at
+the same time. The operation is multi-processed, as opposed to multi-threaded,
+so is not limited by Julia’s thread count. By default, Julia will spawn a
+maximum CPU-balanced load of package precompile jobs at once based on the number
+of CPU cores. Errors during precompilation will only throw for packages listed
+in the Project to allow for dependencies that may be listed in manifests but not
+loaded, and the auto precompilation process will remember if a package has
+errored within the given environment and will not retry until it changes.
+Auto-precompilation can be gracefully interrupted with a `ctrl-c` and disabled
+by setting the environment variable `JULIA_PKG_PRECOMPILE_AUTO=0`.
+
+~~~
+
+~~~
+
+For packages that are being developed, given that their code will be changed by
+other mechanisms than Pkg, this new workflow won’t automatically avoid
+encountering the standard code-load time precompilation. However non-dev-ed
+dependencies of those packages will be kept ready to load, so top-level
+precompilation at load time should remain lower for dev-ed packages.
+
+## Compile time percentage
+
+_Ian Butterworth_
+
+A small change that should help understanding of one of Julia’s quirks for
+newcomers is that the timing macro `@time` and its verbose friend `@timev` now
+report if any of the reported time has been spent on compilation.[^1]
+
+```julia-repl
+julia> x = rand(10,10);
+
+julia> @time x * x;
+ 0.540600 seconds (2.35 M allocations: 126.526 MiB, 4.43% gc time, 99.94% compilation time)
+
+julia> @time x * x;
+ 0.000010 seconds (1 allocation: 896 bytes)
+```
+Given Julia’s Just In Time (JIT) / Just Ahead Of Time (JAOT) compilation, the
+first time code is run the compilation overhead is often substantial, with big
+speed improvements seen in subsequent calls. This change highlights that
+behavior, serving as both a reminder and a tool for rooting out unwanted
+compilation effort i.e. over-specialized code.
+
+[^1]: Note that in some cases the system will look inside the `@time` expression and compile some of the called code before execution of the top-level expression begins. When that happens, some compilation time will not be counted. To include this time you can run `@time @eval ...`
+
+## Eliminating needless recompilation
+
+_Tim Holy_
+
+One of Julia’s most powerful features is its extensibility: you can add new
+methods to previously-defined functions, and use previously-defined methods on
+new types. Sometimes, these new entities force Julia to recompile code to
+account for changes in dispatch. This happens in two steps: first, “outdated”
+code gets _invalidated_, marking it as unsuitable for use; second, as needed the
+code is again compiled from scratch taking account of the new methods and types.
+
+Earlier versions of Julia were somewhat conservative, and invalidated old code
+in some circumstances where there was no actual change in dispatch. Moreover,
+there were many places where Julia and its standard libraries were written in a
+way that defeated Julia’s type-inference. Because the compiler sometimes had to
+invalidate code just because a new method _might_ apply, any uncertainty about
+types magnifies the risk and frequency of invalidation. In older versions of
+Julia, the combination of these effects made invalidation widespread: just
+loading certain packages led to invalidation of up to 10% of Julia’s precompiled
+code. The delay for recompilation could sometimes make interactive sessions feel
+sluggish. When invalidation occurred in Julia’s package-loading code, it also
+delayed loading of the next package, contributing to long waits for `using
+SomePkg` when `SomePkg` depends on other packages.
+
+In 1.6, the scheme for invalidating old code has been made more accurate and
+selective. Moreover, Julia and its standard libraries received a thorough
+makeover to help type inference arrive at a concrete answer more often. The
+result is a leaner, faster Julia that is far more impervious to method
+invalidation, and feels considerably more responsive and nimble in interactive
+sessions. Related blog post: [Analyzing sources of compiler latency in Julia:
+method invalidations](https://julialang.org/blog/2020/08/invalidations/).
+
+## Compiler latency reduction
+
+_Jameson Nash and Jeff Bezanson_
+
+In addition to making our library code more compiler-friendly, we continue
+to try to speed up the compiler itself. This remains one of our main
+technical challenges. In this release there aren't any major
+breakthroughs, but we do have some modest improvements due to work on the
+method table data structure.
+
+Method specificity is a partial order, and prior to 1.6 we stored methods in
+sorted order. We also attempted to identify ambiguous methods on insertion,
+hoping to avoid repeating the work for each future query.
+Unfortunately, sorting a partial order requires quadratic time, and this time
+began to show up prominently during package loading (when a package's methods
+need to be inserted into the currently-active method tables).
+
+We improved things by making the process lazier, moving sorting and ambiguity
+detection into the algorithm for finding matching methods.
+This algorithm runs very often, so it was not at first intuitive that this
+change would help.
+But the key is that the vast majority of queries are for specific enough
+types that most possible matches can be eliminated easily, leaving
+many fewer inputs to the most expensive steps.
+
+The main visible improvement here is to package loading, adding a bit of
+extra speed on top of the gains from addressing invalidations.
+
+There has been substantial effort put into refining our inference quality
+characteristics, both in stopping analysis quickly when it is perceived to not
+be of benefit, and extracting more precise information when possible. Both
+sides of this can have significant benefit for complex code, such as plotting
+libraries, which branch over large numbers of different configuration options.
+
+Much of this benefit should simply be available without doing anything to your
+code except updating Julia version! To go even further, there is now also a
+general framework for profiling compilation times, for investigating what
+functions contribute most heavily to execution latency. This is described in
+more detail by others. But with each release, you may just find that an old
+code pattern, which you used to need to avoid for performance, now works great!
+
+We applied many micro-optimizations to several internal data-structures also.
+These again won't affect how your code works, but should improve how it
+performs dynamically. For example, `invokelatest` is now faster than `try`, and
+nearly as fast as dynamic dispatch. Several complex internal data-structures
+that were trees also became simple hash-tables, improving both their scaling
+performance and making them more cheaply thread-safe. This affects some key
+areas such as type allocations (`apply_type` and `tuple`), method optimization
+lookup (`MethodInstance`), and dispatch (`jl_apply_generic`).
+
+While we haven't yet reached our target performance levels (but, really, can we
+ever get that fast?), we hope this release is a great step forward. There's
+already other work completed in preparation for getting latency down even more
+in the future!
+
+## Tooling to help optimize packages for latency
+
+_Nathan Daly & Tim Holy_
+
+Julia 1.6, in conjunction with SnoopCompile v2.2.0 or higher, features new tools
+for compiler introspection, especially (but not exclusively) for type inference.
+Developers can use the new tools to profile type inference and determine how
+particular package implementation choices interact with compilation time. Early
+adopters have used these tools to eliminate anywhere from a few percent to the
+large majority of first-use latency.
+
+- Related blog post: [Tutorial on precompilation](https://julialang.org/blog/2021/01/precompile_tutorial/)
+- [SnoopCompile.jl documentation](https://timholy.github.io/SnoopCompile.jl/stable/)
+
+## Binary loading speedups
+
+_Elliot Saba & Mosè Giordano_
+
+Providing reliable, portable binaries to packages is a challenge that all
+packaging environments must face, and while Julia's strategy has always been to
+prioritize reliability and reproducibility over all other concerns, in the past
+it has come at a cost. Our solution to the problems of reliability and
+reproducibility was to more fully isolate installed binaries and cross-compile
+them ourselves using the [`BinaryBuilder.jl`](https://github.com/JuliaPackaging/BinaryBuilder.jl)
+framework. Libraries built from `BinaryBuilder.jl` are most often used through
+so-called JLL packages which provide a standardized API that Julia packages can
+use to access the provided binaries. This ease of use and reliability of
+installation resulted in _vastly_ increased load times as compared to the bad
+old days when Julia packages would blindly `dlopen()` libraries and load
+whatever libraries happened to be sitting on the library search path. To
+illustrate the issue, in Julia 1.4, loading the GTK+3 stack required **7
+seconds** when it used to take around **500ms** on the same machine. Through
+many months of hard work and careful investigation, we are pleased to report
+that the same stack of libraries now takes less than **200ms** to load when
+using Julia v1.6 on the same machine.
+
+The cause of this slowdown was multi-faceted and spread across many different
+layers of the Julia ecosystem. Part of the issue was general compiler latency,
+which has been a focus of the compiler team for some time now, as evidenced by
+the compiler latency reduction section in this blog post. Another major piece
+though was general overhead
+incurred by having so many small JLL packages providing bindings; there was
+significant overhead in the loading of each package. In particular, there was
+code inference, code generation and data-structure loading that needed to be
+eliminated if the JLL packages were to be lightweight enough to not affect
+overall load times. In our experiments, we found that one of the largest
+sources of package load times was in the deserialization of backedge
+information, the links from functions in `Base` back to our packages that would
+cause our functions to be recompiled if there was an invalidation effecting
+that `Base` function. As counter-intuitive as it may seem, simply using
+a large number of functions from `Base` can very quickly balloon the
+precompilation cache files for your package, causing an increase in loading
+time! While the increase itself is small, (`3-10ms` at the worst) when you are
+loading many dozens of JLL packages, this adds up quickly.
+
+Our work to slim JLL packages down resulted in the creation of a new package,
+[`JLLWrappers.jl`](https://github.com/JuliaPackaging/JLLWrappers.jl). This
+package provides macros that auto-generate the bindings necessary for a JLL
+package, and do so by using the minimum number of functions and data structures
+possible. By limiting the number of backedges and data structures, as well as
+centralizing the template pieces of code that each JLL package uses, we are
+able to not only vastly improve load times, but improve compile times as well!
+As an added bonus, improvements to JLL package APIs can now be made directly in
+`JLLWappers.jl` without needing to re-deploy hundreds of JLLs. Because these
+JLL packages only define a thin wrapper around simple, lightweight functions
+that load libraries and return paths and such, they do not benefit from the
+heavy optimization that most Julia code undergoes. One final piece of the
+optimization puzzle was therefore to disable optimizations and use the new
+[per-module optimization levels](https://julialang.org/blog/2020/08/julia-1.5-highlights/#per-module_optimization_levels)
+functionality to reduce the amount of time spent generating a very small
+amount of code, saving precious seconds.
+
+~~~
+
+~~~
+
+The interplay between compiler improvements and the benefits that `JLLWrappers`
+affords were [well-recorded](https://github.com/JuliaGraphics/Gtk.jl/issues/466#issuecomment-716058685)
+during the development process, and showcase a speedup of load times for the
+original, non-JLLWrapperized `GTK3_jll` package from its peak at `6.73` seconds
+on Julia v1.4 down to `2.34` seconds on Julia v1.6, purely from compiler
+improvements. If you haven't thanked your local compiler team today, you
+probably should. Using the slimmed-down JLLWrappers implementation of all
+relevant JLLWrappers packages results in a further lowering of load time down
+to a blistering `140ms`. End-to-end, this means that this work effected a
+roughly **`50x` speedup** in load times for large trees of binary artifacts.
+While there are some minor improvements for lazy loading of shared libraries
+and such in the pipeline, we are confident that this work will provide a strong
+foundation for Julia's binary packaging story for the foreseeable future.
+
+## Downloads & NetworkingOptions
+
+_Stefan Karpinski_
+
+In previous releases, when you download something in Julia, either directly,
+using the `Base.download` function, or indirectly when using `Pkg`, the actual
+downloading was done by some external process—whichever one of `curl`, `wget`,
+`fetch` or `PowerShell` happened to be available on your system. The fact that
+this frankendownload feature worked at all was something of a miracle, that only
+worked due to much fussy command-line-option finessing of over the years. And
+while this did mostly work, there were some major drawbacks to this approach.
+
+1. **It’s slow.** Starting a new process for each download is expensive; but
+ worse, those processes can’t share TCP connections or reuse already
+ negotiated TLS connections, so every download needs to do the TCP
+ SYN/ACK song and dance and then also do the TLS secret handshake, all of
+ which takes a lot of time.
+
+2. **It’s inconsistent.** Since the exact way things got downloaded depended on
+ what happens to be installed on your system, download behavior was terribly
+ inconsistent. Downloads that work on one system might not work on another
+ one. Moreover, any issues someone might have, inevitably end up out of scope
+ for Julia to fix — the typical answer is "fix your system
+ `curl`/`wget`/whatever," which is not a very satisfactory solution for someone
+ using Julia who just wants to be able to download things.
+
+3. **It’s inflexible.** The core requirements of downloading something are
+ simple: URL in, file out. But maybe you need to pass some custom headers with
+ the request. Or maybe you need to see what headers were returned. Often you
+ want to display progress for large downloads. Some download commands have
+ options for some of these, but we can only support options that are supported
+ by all download methods, which has forced downloads to be pretty inflexible.
+
+In Julia 1.6 all downloading is done with `libcurl-7.73.0` via the new
+`Downloads.jl` standard library. Downloading is done in-process and TCP+TLS
+connections are shared and reused. If the server supports HTTP/2, multiple
+requests to that server can even be multiplexed onto the same HTTPS connections.
+All of this means that downloads are much faster.
+
+Since all Julia users now use the same method to download things, if it works on
+one system, it is much more likely to work everywhere. No more broken downloads
+just because the system curl happens to be really old. And `libcurl` is highly
+configurable: we can pass custom headers with requests, see what headers were
+included with the response, and get download progress — all in the same way
+everywhere.
+
+As part of reworking downloads, we have switched to using the built-in TLS stack
+on macOS and Windows, which allows downloads to use the built-in mechanism for
+verifying the identity of TLS servers via the system’s collection of certificate
+authority root certificates (“CA roots”, for short). On Linux and FreeBSD, we
+now also look in the standard locations for a PEM file with CA root
+certificates. The advantage of using the system CA root certificates is that
+most systems will automatically keep these CA roots up-to-date and on Windows
+and macOS the OS will check for revoked certificates when performing certificate
+verification (Linux doesn't have standard way to do this). Julia itself still
+ships with a reasonably up-to-date bundle of CA roots, but we no longer use it
+by default unless system CA roots cannot be found.
+
+Using the system CA roots already means that it’s much more likely that Julia
+will “just work” from behind firewalls. Many institutional firewalls will
+man-in-the-middle (MITM) your outgoing HTTPS connections and present a forged
+HTTPS certificate for the server to your client connection. In order for this
+not to set off security alarms on your client, they will typically add a private
+CA root certificate to the user's system so that your browser will accept the
+firewall’s forged certificate. Since Julia now uses the system’s CA roots, it
+respects any private CA roots that have been added there.
+
+If this doesn’t work for some reason, Julia 1.6 also introduces the
+`NetworkOptions.jl` stdlib: this package acts as a central place for network
+configuration options that can be controlled by various environment variables
+and which are used to modify the behavior of networking libraries like `libcurl`
+and `libgit2` in a consistent way. For example, if you want to turn off HTTPS
+host verification entirely, you can do `export JULIA_SSL_NO_VERIFY_HOSTS="**"`
+in your shell and both the Downloads and LibGit2 packages will not perform host
+verification when downloading over HTTPS. There are various other options
+available in NetworkOptions, including:
+
+- `JULIA_SSL_CA_ROOTS_PATH` to provide a custom PEM file of CA roots
+- `SSH_KNOWN_HOSTS_FILES` to use non-standard locations for SSH known hosts
+- `JULIA_*_VERIFY_HOSTS` variables for fine-grained control over which hosts
+ should or shouldn’t be verified over various transports, including TLS and SSH
+
+These options are now consistently respected across all network-facing code that
+ships with Julia itself and we will be working with package developers to
+encourage them to use `NetworkOptions` for configuration of libraries such as
+mbedtls and others. This will allow consistent configuration of networking
+options across the entire Julia ecosystem.
+
+## CI Robustness
+
+_Jeff Bezanson, Keno Fischer, and Jameson Nash_
+
+This release cycle we spent quite a bit of time paying down technical debt in
+the form of intermittent test failures in our continuous integration (CI)
+process. Like all responsible software projects these days, we run our full
+build and test suite for every commit and for every proposed change. If the
+tests fail, you stop the presses until the problem is fixed — either by
+reverting a change, committing a new fix, or revising a proposed patch until it
+passes. Given this simple policy, it’s difficult to see how a project could be
+ambushed by persistent test failures. And yet that’s exactly what happened to
+us: over time, we ended up in a state where a high percentage of test runs
+failed, usually with just a single obscure test case failing.
+
+Several factors contributed to this predicament. First, the base Julia test
+suite is quite large and covers a wide range of functionality, from parsing and
+compiling to linear algebra, package management, sockets, threads, handling file system
+events, and more. With that much surface area, we were likely to end up with a
+handful of rare bugs, or failures due to overly-fragile tests. We run easily
+over a hundred builds per day, so even failures with a rate of 0.1% would appear often
+enough to be disruptive. Timing-sensitive tests are a classic example, e.g.
+testing that a one-second timeout indeed happens after approximately one second.
+On hosted VMs in particular, timing can be far more variable than what you would
+ever see on dedicated hardware. A one-second timeout can, unfortunately, take
+more than 60 seconds on a heavily loaded VM.
+
+After much debugging, including infrastructure work to run tests under
+[rr](https://julialang.org/blog/2020/05/rr) by default, we were able to
+identify and fix many issues. Here is a representative sample:
+
+- [Close a race condition in the FileWatching tests](https://github.com/JuliaLang/julia/pull/38407)
+- [Disarm watchdog timer after Sockets test finishes](https://github.com/JuliaLang/julia/pull/38586)
+- [Remove some overhead to reduce timing variation in Channels test](https://github.com/JuliaLang/julia/pull/38662)
+- [Fix a test for `mktemp` to prevent occasional duplicate names](https://github.com/JuliaLang/julia/pull/38779)
+- [Fix a calling convention issue causing occasional failures on FreeBSD](https://github.com/JuliaLang/julia/pull/38882)
+- [Fix a libunwind issue causing Profile test failures](https://github.com/JuliaLang/julia/pull/39553)
+- [Fix a race in AsyncCondition test](https://github.com/JuliaLang/julia/pull/39583)
+- [Fix occasional deadlock in REPL test](https://github.com/JuliaLang/julia/pull/39482)
+- [Port reuse issue causing occasional Distributed test failure on Darwin](https://github.com/JuliaLang/julia/pull/38901)
+- [Lock leak causing occasional test failure](https://github.com/JuliaLang/julia/pull/38246)
+
+As a result, the proportion of "green check" PRs is noticeably higher.
+We are not yet at 100%, but CI can now generally be expected to pass.
+
+
+## Improved stacktrace formatting
+
+_Kristoffer Carlsson_
+
+For Julia release 0.6, formatting of stacktraces [went through an overhaul](https://julialang.org/blog/2017/04/repl-0.6-highlights/#printing_of_stack_traces)
+and in this release [`@jkrumbiegel`](https://github.com/jkrumbiegel) made further improvements on this area (implemented in [#36134](https://github.com/JuliaLang/julia/pull/36134)).
+Let's look at an example of the old stacktrace printing and compare it to the new one:
+
+**Old stacktrace**:
+
+
+
+
+**New stacktrace**:
+
+
+
+-------------------------
+
+Some improvements are worth pointing out explicitly:
+
+- Argument names in methods are now shown.
+- The function name was made to be more emphasized compared to the surrounding text, since that information tends to be the most important.
+- The module where the method is defined is now shown and the modules are also color coded.
+- Paths to the method were de-emphasized since they usually have lower importance.
+- Paths were made shorter by showing `~` instead of the full path to the home directory.
+
+
+## Conclusion
+
+Please enjoy the release, and as always [let us know](https://github.com/JuliaLang/julia/issues)
+if you encounter any problems or have any suggestions.
+We hope to be back in about four months to report on even more progress in version 1.7!