In the first part of this blog post , I explained how the Matrix protocol works, contrasted its design philosophy with XMPP, and discussed why these differences lead to performance costs in Matrix. Matrix processes each conversation as a graph of events, merged in real-time ^[1] .

Merge operations can be costly in Matrix for large rooms, affecting both database storage and load and disk usage when memory is exhausted, reaching swap level .

That said, there is still room for improvement in the protocol. We have designed and tested slight changes that could make Matrix much more efficient for large rooms.

A Proposal to Simplify and Speed Up Merge Operations

Here is the rationale behind a proposal we have made to simplify and speed up merge operations:

State resolution v2 uses certain graph algorithms, which can result in at least linear processing time for the number of state events in a room’s DAG, creating a significant load on servers.

The goal of this issue is to discuss and develop changes to state resolution to achieve O(n log ⁡ n) total processing time when handling a room with n state events (i.e., O(log ⁡ n) on average) in realistic scenarios, while maintaining a good user experience.

The approach described below is closer to state resolution v1 but seeks to address state resets in a different way.

For more detail, you can read our proposal on the Matrix spec tracker: Make state resolution faster .

In simpler terms, we propose adding a version associated with each event_id to simplify conflict management and introduce a heuristic that skips traversal of large parts of the graph.

Impact of the Proposal

From our initial assessment, in a very large room — such as one with 100,000 members — our approach could improve processing performance by 100x to 1000x, as the current processing cost scales with the number of users in the room. This improvement would enable smoother conversations, reduced lag, and more responsive interactions for end-users, while also reducing server infrastructure load and resource usage.

While our primary goal is to improve performance in very large rooms, these changes benefit all users by reducing overall server load and improving processing times across various room sizes.

We plan to implement this improvement in our own code to evaluate its real-world effectiveness while the Matrix team considers its potential value for the reference protocol.

---

1. For those who remember, a conversation in Matrix is similar to the collaborative editing protocol built on top of XMPP for the Google Wave platform.

The Ignite Realtime community is happy to be able to announce the immediate availability of version 4.9.1 of Openfire , its cross-platform real-time collaboration server based on the XMPP protocol!

4.9.1 is a bugfix and maintenance release. Among its most important fixes is one for a memory leak that affected all recent versions of Openfire (but was likely noticeable only on those servers that see high volume of users logging in and out). The complete list of changes that have gone into this release can be seen in the change log .

Please give this version a try! You can download installers of Openfire here . Our documentation contains an upgrade guide that helps you update from an older version.

The integrity of these artifacts can be checked with the following sha256sum values:

8c489503f24e35003e2930873037950a4a08bc276be1338b6a0928db0f0eb37d  openfire-4.9.1-1.noarch.rpm
1e80a119c4e1d0b57d79aa83cbdbccf138a1dc8a4086ac10ae851dec4f78742d  openfire_4.9.1_all.deb
69a946dacd5e4f515aa4d935c05978b5a60279119379bcfe0df477023e7a6f05  openfire_4_9_1.dmg
c4d7b15ab6814086ce5e8a1d6b243a442b8743a21282a1a4c5b7d615f9e52638  openfire_4_9_1.exe
d9f0dd50600ee726802bba8bc8415bf9f0f427be54933e6c987cef7cca012bb4  openfire_4_9_1.tar.gz
de45aaf1ad01235f2b812db5127af7d3dc4bc63984a9e4852f1f3d5332df7659  openfire_4_9_1_x64.exe
89b61cbdab265981fad4ab4562066222a2c3a9a68f83b6597ab2cb5609b2b1d7  openfire_4_9_1.zip

We would love to hear from you! If you have any questions, please stop by our community forum or our live groupchat . We are always looking for volunteers interested in helping out with Openfire development!

For other release announcements and news follow us on Mastodon or X

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Adopting machine learning for business is necessary for companies that want to sharpen their competitive industries. With the global market for machine learning projected to reach an impressive $210 billion by 2030 , businesses are keen to seek active solutions that streamline processes and improve customer interactions.

While organisations may already employ some form of data analysis, traditional methods can need more sophistication to address the complexities of today’s market. Businesses that consider optimising machines unlock valuable data insights, make accurate predictions and deliver personalised experiences that truly resonate with customers, ultimately driving growth and efficiency.

What is Machine Learning?

Machine learning (ML) is a subset of artificial intelligence (AI). It uses machine learning algorithms , designed to learn from data, identify patterns, and make predictions or decisions, without explicit programming. By analysing patterns in the data, a machine learning algorithm identifies key features that define a particular data point, allowing it to apply this knowledge to new, unseen information.

Fundamentally data-driven, machine learning relies on vast information to learn, adapt, and improve over time. Its predictive capabilities allow models to forecast future outcomes based on the patterns they uncover. These models are generalisable, so they can apply insights from existing data to make decisions or predictions in unfamiliar situations.

You can read more about machine learning and AI in our previous post .

Approaches to Machine Learning

Machine learning for business typically involves two key approaches: supervised and unsupervised learning , each suited to different types of problems. Below, we explain each approach and provide examples of machine learning use cases where these techniques are applied effectively.

• Supervised Machine Learning: This approach demands labelled data, where the input is matched with the correct output. The algorithms learn to map inputs to outputs based on this training set, honing their accuracy over time.
• Unsupervised Machine Learning: In contrast, unsupervised learning tackles unlabelled data, compelling the algorithm to uncover patterns and structures independently. This method can involve tasks like clustering and dimensionality reduction. While unsupervised techniques are powerful, interpreting their results can be tricky, leading to challenges in assessing whether the model is truly on the right track.

Example of Supervised vs unsupervised learning

Supervised learning uses historical data to make predictions, helping businesses optimise performance based on past outcomes. For example, a retailer might use supervised learning to predict customer churn . By feeding the algorithm data such as customer purchase history and engagement metrics, it learns to identify patterns that indicate a high risk of churn, allowing the business to implement proactive retention strategies.

Unsupervised learning , on the other hand, uncovers hidden patterns within data. It is particularly useful for discovering new customer segments without prior labels. For instance, an e-commerce platform might use unsupervised learning to group customers by their browsing habits, discovering niche audiences that were previously overlooked.

The Impact of Machine Learning on Business

A recent survey by McKinsey revealed that 56% of organisations surveyed are using machine learning in at least one business function to optimise their operations. This growing trend shows how machine learning for business is becoming integral to staying competitive.

The AI market as a whole is also on an impressive growth trajectory, projected to reach USD 407.0 billion by 2027 .

AI Global Market Forecast to 2030

We’re expected to see an astounding compound growth rate (CAGR) of 35.7% by 2030, proving that business analytics is no longer just a trend; it’s moving into a core component of modern enterprises.

Machine Learning for Business Use Cases

Machine learning can be used in numerous ways across industries to enhance workflows. From image recognition to fraud detection , businesses are actively using AI to streamline operations.

Image Recognition

Image recognition, or image classification is a powerful machine learning technique used to identify and classify objects or features in digital images.

Artificial intelligence (AI) and machine learning (ML) are revolutionising image recognition systems by uncovering hidden patterns in images that may not be visible to the human eye. This technology allows these systems to make independent and informed decisions, significantly reducing the reliance on human input and feedback.

As a result, visual data streams can be processed automatically at an ever-increasing scale, streamlining operations and enhancing efficiency. By harnessing the power of AI, businesses can leverage these insights to improve their decision-making processes and gain a competitive edge in their respective markets.

It plays a crucial role in tasks like pattern recognition, face detection, and facial recognition, making it indispensable in security and social media sectors.

Fraud Detection

With financial institutions handling millions of transactions daily, distinguishing between legitimate and fraudulent activity can be a challenge. As online banking and cashless payments grow, so too has the volume of fraud. A 2023 report from TransUnion revealed a 122% increase in digital fraud attempts in the US between 2019 and 2022.

Machine learning helps businesses by flagging suspicious transactions in real-time, with companies like Mastercard using AI to predict and prevent fraud before it occurs, protecting consumers from potential theft.

Speech Recognition

Voice commands have become a common feature in smart devices, from setting timers to searching for shows.

Thanks to machine learning, devices like Google Nest speakers and Amazon Blink security systems can recognise and act on voice inputs, making hands-free operation more convenient for users in everyday situations.

Improved Healthcare

Machine learning in healthcare has led to major improvements in patient care and medical discoveries. By analysing vast amounts of healthcare data, machine learning enhances the accuracy of diagnoses, optimises treatments, and accelerates research outcomes.

For instance, AI systems are already employed in radiology to detect diseases in medical images, such as identifying cancerous growths. Additionally, machine learning is playing a crucial role in genomic research by uncovering patterns linked to genetic disorders and potential therapies. These advancements are paving the way for improved diagnostics and faster medical research, offering tremendous potential for the future of healthcare.

Key applications of machine learning in healthcare include:

• Developing predictive modelling
• Improving diagnostic accuracy
• Personalising patient care
• Automating clinical workflows
• Enhancing patient interaction

Machine learning in healthcare utilises algorithms and statistical models to analyse large medical datasets, facilitating better decision-making and personalised care. As a subset of AI, machine learning identifies patterns, makes predictions, and continuously improves by learning from data. Different types of learning, including supervised and unsupervised learning, find applications in disease classification and personalised treatment recommendations.

Chatbots

Many businesses rely on customer support to maintain satisfaction. However, staffing trained specialists can be expensive and inefficient. AI-powered chatbots, equipped with natural language processing (NLP), assist by handling basic customer queries. This frees up human agents to focus on more complicated issues. Companies can provide more efficient and effective support without overburdening their teams.

Each of these applications offers businesses the chance to streamline operations and improve customer experiences.

Machine Learning Case Studies

Machine learning for business is transforming industries by enabling companies to enhance their operations, improve customer experiences, and drive innovation.

Here are a few machine learning case studies showing how leading organisations have integrated machine learning into their business strategies.

PayPal

PayPal, a worldwide payment platform, faced huge challenges in identifying and preventing fraudulent transactions.

To tackle this issue, the company implemented machine learning algorithms designed for fraud detection . These algorithms analyse various aspects of each transaction, including the transaction location, the device used, and the user’s historical behaviour. This approach has significantly enhanced PayPal’s ability to protect users and maintain the integrity of its payment platform.

YouTube

YouTube has long employed machine learning to optimise its operations, particularly through its recommendation algorithms . By analysing vast amounts of historical data, YouTube suggests videos to its viewers based on their preferences. Currently, the platform processes over 80 billion data points for each user, requiring large-scale neural networks that have been in use since 2008 to effectively manage this immense dataset.

Dell

Recognising the importance of data in marketing, Dell’s marketing team sought a data-driven solution to enhance response rates and understand the effectiveness of various words and phrases. Dell partnered with Persado, a firm that leverages AI to create compelling marketing content. This collab led to an overhaul of Dell’s email marketing strategy, resulting in a 22% average increase in page visits and a 50% boost in click-through rates (CTR). Dell now utilises machine learning methods to refine its marketing strategies across emails, banners, direct mail, Facebook ads, and radio content.

Tesla

Tesla employs machine learning to enhance the performance and features of its electric vehicles. A key application is its Autopilot system , which combines cameras, sensors, and machine learning algorithms to provide advanced driver assistance features such as lane centring, adaptive cruise control, and automatic emergency braking.

The Autopilot system uses deep neural networks to process vast amounts of real-world driving data, enabling it to predict driving behaviour and identify potential hazards. Additionally, Tesla leverages machine learning in its battery management systems to optimise battery performance and longevity by predicting behaviour under various conditions.

Netflix

Netflix is a leader in personalised content recommendations. It uses machine learning to analyse user viewing habits and suggest shows and movies tailored to individual preferences. This feature has proven essential for improving customer satisfaction and increasing subscription renewals. To develop this system, Netflix utilises viewing data—including viewing durations, metadata, release dates, timestamps etc. Netflix then employs collaborative filtering, matrix factorisation, and deep learning techniques to accurately predict user preferences.

Benefits of Machine Learning in Business

If you’re still contemplating the value of machine learning for your business, consider the following key benefits:

Automation Across Business Processes	Machine learning automates key business functions, from marketing to manufacturing, boosting yield by up to 30%, reducing scrap, and cutting testing costs. This frees employees from more creative, strategic tasks.
Efficient Predictive Maintenance	ML helps manufacturing predict equipment failures, reducing downtime and extending machinery lifespan, ensuring operational continuity.
Enhanced Customer Experience and Accurate Sales Forecasts	Retailers use machine learning to analyse consumer behaviour, accurately forecast demand, and personalise offers, greatly improving customer experience.
Data-Driven Decision-Making	ML algorithms quickly extract insights from data, enabling faster, more informed decision-making and helping businesses develop effective strategies.
Error Reduction	By automating tasks, machine learning reduces human error, so employees to focus on complex tasks, significantly minimising mistakes.
Increased Operational Efficiency	Automation and error reduction from ML lead to efficiency gains. AI systems like chatbots boost productivity by up to 54%, operating 24/7 without fatigue.
Enhanced Decision-Making	ML processes large data sets swiftly, turning information into objective, data-driven decisions, removing human bias and improving trend analysis.
Addressing Complex Business Issues	Machine learning tackles complex challenges by streamlining operations and boosting performance, enhancing productivity and scalability.

As organisations increasingly adopt machine learning, they position themselves to not only meet current demands but poise them for future innovation.

Elixir and Erlang in Machine Learning

As organisations explore machine learning tools, many are turning to Erlang and Elixir programming languages to develop customised solutions that cater to their needs. Erlang’s fault tolerance and scalability make it ideal for AI applications, as described in our blog on adopting AI and machine learning for business . Additionally, Elixir’s concurrency features and simplicity enable businesses to build high-performance AI applications.

Learn more about how to build a machine-learning project in Elixir here .

As organisations become more familiar with AI and machine learning tools, many are turning to Erlang and Elixir programming languages to develop customised solutions that cater to their needs.

Elixir, built on the Erlang virtual machine (BEAM), delivers top concurrency and low latency. Designed for real-time, distributed systems, Erlang prioritises fault tolerance and scalability, and Elixir builds on this foundation with a high-level, functional programming approach. By using pure functions and immutable data, Elixir reduces complexity and minimises unexpected behaviours in code. It excels at handling multiple tasks simultaneously, making it ideal for AI applications that need to process large amounts of data without compromising performance.

Elixir’s simplicity in problem-solving also aligns perfectly with AI development, where reliable and straightforward algorithms are essential for machine learning. Furthermore, its distribution features make deploying AI applications across multiple machines easier, meeting the high computational demands of AI systems.

With a rich ecosystem of libraries and tools, Elixir streamlines development, so AI applications are scalable, efficient, and reliable. As AI and machine learning become increasingly vital to business success, creating high-performing solutions will become a key competitive advantage.

Final Thoughts

Embracing machine learning for business is no longer optional for companies that want to remain competitive. Machine learning tools empower businesses to make faster, data-driven decisions, streamline operations, and offer personalised customer experiences. Contact the Erlang Solutions team today if you’d like to discuss building AI systems using Elixir and Erlang or for more insights into implementing machine learning solutions,

The post Why you should consider machine learning for business appeared first on Erlang Solutions .

We’re excited to announce ejabberd 24.10, a major release packed with substantial improvements and support for important extensions specified by the XMPP Standard Foundation (XSF). This release represents three months of focused development, bringing around 100 commits to the core repository alongside key updates in dependencies. The improvements span enhanced security, streamlined connectivity, and new administrative tools—all designed to make ejabberd more powerful and easier to use than ever.

Release Highlights:

• XEP-0288: Bidirectional Server-to-Server Connections
• XEP-0480: SASL Upgrade Tasks
• IQ permission in privileged entities
• PubSub varied fixes
• WebAdmin improvements

If you are upgrading from a previous version, please note minor changes in commands and two changes in hooks . There are no configuration or SQL schema changes in this release.

Below is a detailed breakdown of the new features, fixes, and enhancements:

Support for XEP-0288: Bidirectional Server-to-Server Connections

The new mod_s2s_bidi module introduces support for XEP-0288: Bidirectional Server-to-Server Connections . This update removes the requirement for two connections per server pair in XMPP federations, allowing for more streamlined inter-server communications. However, for full compatibility, ejabberd can still connect to servers that do not support bidirectional connections, using two connections when necessary. The module is enabled by default in the sample configuration.

Support for XEP-0480: SASL Upgrade Tasks

The new mod_scram_upgrade module implements XEP-0480: SASL Upgrade Tasks . Compatible clients can now automatically upgrade encrypted passwords to more secure formats, enhancing security with minimal user intervention.

PubSub Service Improvements

We’ve implemented six noteworthy fixes to improve PubSub functionality:

• PEP notifications are sent only to owners when +notify ( 3469a51 )
• Non-delivery errors for locally generated notifications are now skipped ( d4b3095 )
• Fix default node config parsing ( b439929 )
• Fix merging of default node options ( ca54f81 )
• Fix choice of node config defaults ( a9583b4 )
• Fall back to default plugin options ( 36187e0 )

IQ permission for privileged entities

The mod_privilege module now supports IQ permission based on version 0.4 of XEP-0356: Privileged Entity . See #3889 for details. This feature is especially useful for XMPP gateways using the Slidge library.

WebAdmin improvements

ejabberd 24.06 release laid the foundation for a more streamlined WebAdmin interface, reusing existing commands instead of using specific code, with a possibly different logic. This major change allows developers to add new pages very fast, just by calling existing commands. It also allows administrators to use the same commands than in ejabberdctl or any other command frontend .

As a result, many new pages and content were added. Building on that, the 24.10 update introduces MAM (Message Archive Management) support, allowing administrators to view message counts, remove all MAM messages, or only for a specific contact, and also view the MAM Archive directly from WebAdmin.

Additionally, WebAdmin now hides pages related to modules that are disabled, preventing unnecessary options from displaying. This affects mod_last, mod_mam, mod_offline, mod_privacy, mod_private, mod_roster, mod_vcard.

Fixes in commands

• set_presence : Now returns an error when the session is not found.

• send_direct_invitation : Improved handling of malformed JIDs.

• update : Fix command output. So far, ejabberd_update:update/0 returned the return value of release_handler_1:eval_script/1 . That function returns the list of updated but unpurged modules, i.e., modules where one or more processes are still running an old version of the code. Since commit 5a34020d23f455f80a144bcb0d8ee94770c0dbb1 , the ejabberd update command assumes that value to be the list of updated modules instead. As that seems more useful, modify ejabberd_update:update/0 accordingly. This fixes the update command output.

• get_mam_count : New command to retrieve the number of archived messages for a specific account.

Changes in hooks

Two key changes in hooks:

• New check_register_user hook in ejabberd_auth.erl to allow blocking account registration when a tombstone exists.

• Modified room_destroyed hook in mod_muc_room.erl . Until now the hook passed as arguments: LServer, Room, Host . Now it passes: LServer, Room, Host, Persistent That new Persistent argument passes the room persistent option, required by mod_tombstones because only persistent rooms should generate a tombstone, temporary ones should not. And the persistent option should not be completely overwritten, as we must still known its real value even when room is being destroyed.

Log Erlang/OTP and Elixir versions

During server start, ejabberd now shows in the log not only its version number, but also the Erlang/OTP and Elixir versions being used. This will help the administrator to determine what software versions are being used, which is specially useful when investigating some problem, and explaining it to other people for help.

The ejabberd.log file now looks like this:

...
2024-10-22 13:47:05.424 [info] Creating Mnesia disc_only table &aposoauth_token&apos
2024-10-22 13:47:05.427 [info] Creating Mnesia disc table &aposoauth_client&apos
2024-10-22 13:47:05.455 [info] Waiting for Mnesia synchronization to complete
2024-10-22 13:47:05.591 [info] ejabberd 24.10 is started in the node :ejabberd@localhost in 1.93s
2024-10-22 13:47:05.606 [info] Elixir 1.16.3 (compiled with Erlang/OTP 26)
2024-10-22 13:47:05.606 [info] Erlang/OTP 26 [erts-14.2.5.4] [source] [64-bit] [smp:4:4] [ds:4:4:10] [async-threads:1] [jit:ns]

2024-10-22 13:47:05.608 [info] Start accepting TCP connections at 127.0.0.1:7777 for :mod_proxy65_stream
2024-10-22 13:47:05.608 [info] Start accepting UDP connections at [::]:3478 for :ejabberd_stun
2024-10-22 13:47:05.608 [info] Start accepting TCP connections at [::]:1883 for :mod_mqtt
2024-10-22 13:47:05.608 [info] Start accepting TCP connections at [::]:5280 for :ejabberd_http
...

Brand new ProcessOne and ejabberd web sites

We’re excited to unveil the redesigned ProcessOne website, crafted to better showcase our expertise in large-scale messaging across XMPP, MQTT, Matrix, and more. This update highlights our core mission of delivering scalable, reliable messaging solutions, with a fresh layout and streamlined structure that reflect our cutting-edge work in the field.

You now get a cleaner ejabberd page , offering quick access to important URLs for downloads, blog posts, and documentation.

Behind the scenes, we’ve transitioned from WordPress to Ghost, a move inspired by its efficient, user-friendly authoring tools and long-term maintainability. All previous blog content has been preserved, and with this new setup, we’re poised to deliver more frequent updates on messaging, XMPP, ejabberd, and related topics.

We welcome your feedback—join us on our new site to share your thoughts, or let us know about any issue or broken link!

Acknowledgments

We would like to thank the contributions to the source code, documentation, and translation provided for this release by:

• Holger Weiß for PubSub and other fixes
• Michael Slezak for mix release fix
• Guus der Kinderen for the XMPP Interop tests
• Sketch6580 , updated the Chinese translation
• Wellington Uemura , updated the Portuguese (Brazil) translation
• Ermete Melchiorre , updated the Italian translation
• Mr. EddX , updated the Bulgarian translation

And also to all the people contributing in the ejabberd chatroom, issue tracker...

Improvements in ejabberd Business Edition

Customers of the ejabberd Business Edition , in addition to all those improvements and bugfixes, also get MUC support in mod_unread .

ejabberd keeps a counter of unread messages per conversation using the mod_unread module. This now also works in MUC rooms: each user can retrieve the number of unread messages in each of their rooms.

ChangeLog

This is a more detailed list of changes in this ejabberd release:

Miscelanea

• ejabberd_c2s : Optionally allow unencrypted SASL2
• ejabberd_system_monitor : Handle call by gen_event:swap_handler ( #4233 )
• ejabberd_http_ws : Remove support for old websocket connection protocol
• ejabberd_stun : Omit auth_realm log message
• ext_mod : Handle info message when contrib module transfers table ownership
• mod_block_strangers : Add feature announcement to disco-info ( #4039 )
• mod_mam : Advertise XEP-0424 feature in server disco-info ( #3340 )
• mod_muc_admin : Better handling of malformed jids in send_direct_invitation command
• mod_muc_rtbl : Fix call to gen_server:stop ( #4260 )
• mod_privilege : Support "IQ permission" from XEP-0356 0.4.1 ( #3889 )
• mod_pubsub : Don&apost blindly echo PEP notification
• mod_pubsub : Skip non-delivery errors for local pubsub generated notifications
• mod_pubsub : Fall back to default plugin options
• mod_pubsub : Fix choice of node config defaults
• mod_pubsub : Fix merging of default node options
• mod_pubsub : Fix default node config parsing
• mod_register : Support to block IPs in a vhost using append_host_config ( #4038 )
• mod_s2s_bidi : Add support for S2S Bidirectional
• mod_scram_upgrade : Add support for SCRAM upgrade tasks
• mod_vcard : Return error stanza when storage doesn&apost support vcard update ( #4266 )
• mod_vcard : Return explicit error stanza when user attempts to modify other&aposs vcard
• Minor improvements to support mod_tombstones (#2456)
• Update fast_xml to use use_maps and remove obsolete elixir files
• Update fast_tls and xmpp to improve s2s fallback for invalid direct tls connections
• make-binaries : Bump dependency versions: Elixir 1.17.2, OpenSSL 3.3.2, ...

Administration

• ejabberdctl : If ERLANG_NODE lacks host, add hostname ( #4288 )
• ejabberd_app : At server start, log Erlang and Elixir versions
• MySQL: Fix column type in the schema update of archive table in schema update

Commands API

• get_mam_count : New command to get number of archived messages for an account
• set_presence : Return error when session not found
• update : Fix command output
• Add mam and offline tags to the related purge commands

Code Quality

• Fix warnings about unused macro definitions reported by Erlang LS
• Fix Elvis report: Fix dollar space syntax
• Fix Elvis report: Remove spaces in weird places
• Fix Elvis report: Don&apost use ignored variables
• Fix Elvis report: Remove trailing whitespace characters
• Define the types of options that opt_type.sh cannot derive automatically
• ejabberd_http_ws : Fix dialyzer warnings
• mod_matrix_gw : Remove useless option persist
• mod_privilege : Replace try...catch with a clean alternative

Development Help

• elvis.config : Fix file syntax, set vim mode, disable many tests
• erlang_ls.config : Let it find paths, update to Erlang 26, enable crossref
• hooks_deps : Hide false-positive warnings about gen_mod
• Makefile : Add support for make elvis when using rebar3
• .vscode/launch.json : Experimental support for debugging with Neovim
• CI: Add Elvis tests
• CI: Add XMPP Interop tests
• Runtime: Cache hex.pm archive from rebar3 and mix

Documentation

• Add links in top-level options documentation to their Docs website sections
• Document which SQL servers can really use update_sql_schema
• Improve documentation of ldap_servers and ldap_backups options ( #3977 )
• mod_register : Document behavior when access is set to none ( #4078 )

Elixir

• Handle case when elixir support is enabled but not available
• Start ExSync manually to ensure it&aposs started if (and only if) Relive
• mix.exs : Fix mix release error: logger being regular and included application ( #4265 )
• mix.exs : Remove from extra_applications the apps already defined in deps ( #4265 )

WebAdmin

• Add links in user page to offline and roster pages
• Add new "MAM Archive" page to webadmin
• Improve many pages to handle when modules are disabled
• mod_admin_extra : Move some webadmin pages to their modules

Full Changelog

https://github.com/processone/ejabberd/compare/24.07...24.10

ejabberd 24.10 download & feedback

As usual, the release is tagged in the Git source code repository on GitHub .

The source package and installers are available in ejabberd Downloads page. To check the *.asc signature files, see How to verify ProcessOne downloads integrity .

For convenience, there are alternative download locations like the ejabberd DEB/RPM Packages Repository and the GitHub Release / Tags .

The ecs container image is available in docker.io/ejabberd/ecs and ghcr.io/processone/ecs . The alternative ejabberd container image is available in ghcr.io/processone/ejabberd .

If you consider that you&aposve found a bug, please search or fill a bug report on GitHub Issues .

When I began working with Phoenix LiveView , the project evolved from a simple backend service into a powerful, UI-driven customer service tool. A basic Phoenix app for storing user data quickly became a core part of our client’s workflow.

In this post, I’ll take you through a project that grew from its original purpose- from a service for storing and serving user data to a LiveView-powered application that is now a key tool in the client’s organisation for customer service.

Why We Chose Phoenix LiveView

Our initial goal was to migrate user data from an external, paid service to a new in-house solution, developed collaboratively by Erlang Solutions (ESL) and the client’s teams.

With millions of users, we needed a simple way to verify migrated data without manually connecting to the container and querying the database every time.

Since the in-house service was a Phoenix application that uses Ecto and Postgres, adding LiveView was the most natural fit.

Implementing Phoenix LiveView: Data Migration and UI Development

After we had established the goal, the next step was to create a database service to store and serve user information to other services, as well as to migrate all existing user data from an external service to the new one.

We chose Phoenix with Ecto and Postgres, as the old database was already connected to a Phoenix application , and the client’s team was well-versed in Elixir and BEAM .

Data Migration Strategy

The ESL and client teams’ strategy began by slowly copying user data from the old service to the new database whenever users logged in. For certain users (e.g., developers), we logged them in and pulled user information only from the new system. We defined a new login session struct (Elixir struct), which we used for pattern matching to determine whether to use the old or new system. The old system was treated as a fallback and the source of truth for user data.

With this strategy, we could develop and test the new database system in parallel with the old one in production, without affecting regular users, and ensured that everything worked as expected.

At the end, we performed a data dump for all users, configuring the service to use the new system as the main source of truth. Since we had tested with a small number of users beforehand, the transition was smooth, and users had no idea anything had changed from their end. Response times were cut in half compared to the previous solution!

The Evolution of LiveView Application

The addition of LiveView to the application was first thought of when the ESL team together with the client team wanted to check the test migration data. The team wanted to be able to cross reference immediately if the user data has been inserted or updated as intended in our new service. It was complicated and cumbersome at first as we had to connect to the application remotely and do a manual query or call an internal function from a remote Elixir shell.

Initially, LiveView was developed solely for the team. We started with a simple table listing users, then added search functionality for IDs or emails, followed by pagination as the test data grew. With the simple UI using LiveView in place, we started with the data migration process and the UI helped tremendously when we went to verify if the data got migrated correctly, and how many users we have successfully migrated.

Adoption and Expansion of the LiveView Tool

As we demonstrated the UI to stakeholders, it quickly became the go-to tool for customer service, with new features continuously added based on feedback. The development team received many requests from customer service and other managers in the client’s organisation. We fulfilled these requests with features such as searching users by a combination of fields, helping change users’ email addresses, and checking user activity (e.g., when a user’s email was changed or if users suspected they had been hacked).

Later, we connected the LiveView application to sync and display data from another internal service, which contained information about users’ access to the client’s product. The customer service team was able to get a more complete view of the user and could use the same tool to grant or sync user access without switching to other systems.

The best aspect of using Phoenix LiveView is that the development team also owned the UI. We determined the data structure, knew what needed to be there, and designed the LiveView page ourselves. This removed the need to rely on another team, and we could reflect changes swiftly in the web views without having to coordinate with external teams.

Challenges and Feedback of Implementing Phonenix LiveView

There were some glitches along the way, and when we asked for feedback from the customer service team, we found several UX aspects that could be improved. For example, data didn’t always update immediately, or buttons occasionally failed to work properly. However, these issues also indicated that the Phoenix LiveView application was used heavily by the team, emphasising the need for improvements to support better workflows.

While our LiveView implementation worked well, it wasn’t without imperfections. Most of our development team lacked extensive web development experience, so there were several aspects we either overlooked or didn’t fully consider. Some team members had limited knowledge of web technologies like Tailwind and CSS/HTML, which helped guide us, but we realised that for a more polished user experience (UX) and smoother interface, basic HTML/CSS skills alone wouldn’t be sufficient to create an optimal LiveView application.

Another challenge was infrastructure. Since our service was read-heavy, we used AWS RDS reader instances to maximise performance, but this led to occasional replication delays. These delays could cause mismatches when customer service updated data and LiveView reloaded the page before the updates had replicated to the reader instances. We had to carefully consider when it was appropriate to use the reader instances and adjust our approach accordingly.

Team Dynamics and Collaboration

Mob programming way of working was also one of the factors that led to the success of this project.  Our team consists of members with different expertise. By working together, we can discuss and share our experiences while programming together, instead of having to explain later in code review or knowledge sharing what each of us has implemented and why. For example, we guided a member who had more experience in Erlang/OTP through creating a form with Liveview, which needed more experience in Ecto and Phoenix. That member could then explain and guide others with OTP-related implementation in our services.

Mob programming helped our team focus on one large task at a time. This collaborative approach ensured a consistent codebase with unified conventions, leading to efficient feature implementation.

Conclusion

What began as a simple backend project with Phoenix and Ecto evolved into a key tool for customer service, driven by the power of Phoenix LiveView. The Admin page, initially unplanned, became an integral part of the client’s workflow, proving the vast potential of LiveView and Elixir.

Though we encountered challenges, LiveView’s real-time interactivity, seamless integration, and developer control over both the backend and UI were invaluable. We believe we’ve only scratched the surface of what developers can achieve with LiveView.

Want to learn more about LiveView? Check out this article . If you’re exploring Phoenix LiveView for your project, feel free to reach out —we’d love to share our experience and help you unlock its full potential.

The post Implementing Phoenix LiveView: From Concept to Production appeared first on Erlang Solutions .

We’re excited to announce the relaunch of our website, designed to better showcase our expertise in large-scale messaging solutions, highlighting our full spectrum of supported protocols—from XMPP to MQTT and Matrix. This reflects our core strength: delivering reliable messaging at scale.

The last major redesign was back in October 2017, so this update was long overdue. As we say farewell to the old design, here’s a screenshot of the previous version to commemorate the journey so far.

In addition to refreshing the layout and structure, we’ve made a significant change under the hood by migrating from WordPress to Ghost. After using Ghost for my personal blog and being thoroughly impressed, we knew it was the right choice for ProcessOne. The new platform offers not only long-term maintainability but also a much more streamlined, enjoyable day-to-day experience, thanks to its faster and more efficient authoring tools.

All of our previous blog content has been successfully migrated, and we’re now in a great position to deliver more frequent updates on topics such as messaging, XMPP, ejabberd, MQTT, and Matrix. Stay tuned for exciting new posts!

We’d love to hear your feedback and suggestions on what topics you’d like us to cover next. To join the conversation, simply create an account on our site and share your thoughts.

At Erlang Solutions, we’ve worked with diverse clients, solving business challenges and delivering impactful results. We would like to share just some of our top client case studies in this latest post with you.

Get a glimpse into how our leading technologies—Erlang, Elixir, MongooseIM, and more—combined with our expert team, have transformed the outcomes for major industry players.

Transforming streaming with zero downtime for TV4

Our first client case study is our partnership with TV4 . The leading Nordic broadcaster needed to address major challenges in the competitive streaming industry. With global giants like Netflix and Disney Plus on the rise, TV4 needed to unify user data from multiple platforms into a seamless streaming experience for millions of subscribers.

Using Elixir, we ensured a smooth migration and helped TV4 reduce infrastructure costs and improve efficiency.

Check out the full TV4 case study .

Financial services with secure messaging solutions with Teleware

Erlang Solutions partnered with Teleware to enhance their Reapp with secure instant messaging (IM) capabilities for a major UK financial services group. As TeleWare aimed to meet strict regulatory requirements while improving user experience, they needed a robust, scalable solution that could seamlessly integrate into their existing infrastructure.

We utilised MongooseIM ’s out-of-the-box functionality, and Teleware quickly integrated group chat features that allowed secure collaboration while meeting the Financial Conduct Authority (FCA) compliance standards.

Take a look at the full Teleware case study .

Gaming experiences with enhanced scalability and performance for FACEIT

FACEIT , the leading independent competitive gaming platform with over 25 million users, had some scalability and performance challenges. As its user base grew, FACEIT needed to upgrade their systems to handle hundreds of thousands of players seamlessly.

By upgrading to the latest version of MongooseIM and Erlang , we delivered a solution that managed large user lists and improved overall system efficiency.

Explore the full FACEIT case study .

Rapid growth with scalable systems for BET Software

In another one of our client case studies, we worked with BET Software , a leading betting software provider in South Africa, to address the challenges posed by rapid growth and increasing user demand. As the main technology provider for Hollywoodbets, BET Software needed a more resilient and scalable system to support peak betting periods.

By utilising Elixir to support and transition to a distributed data architecture, we helped BET Software eliminate bottlenecks and ensure seamless service- even during the busiest betting events.

Read the BET Software case study in full.

Innovation and competitive edge with International Registries Inc.

The final client case study of this series is with International Registries Inc. (IRI) . They are global leaders in maritime and corporate registry services, who were looking to enhance its technological infrastructure and strengthen their competitive advantage.

Erlang Solutions helped IRI by using Elixir to reduce costs, improve system maintainability, and decommission servers.

Discover the complete IRI case study.

Real results from client case studies

Our client case study examples show how we help companies like TV4, FACEIT, TeleWare, BET Software, and International Registries Inc. solve tough tech challenges and excel in competitive markets. Whether it’s boosting performance, securing communications, or scaling for growth, our solutions unlock new possibilities.

You can explore more Erlang Solutions case studies here .

If you’d like to chat with the Erlang Solutions team about what we can do for you, feel free to drop us a message .

The post Client Case Studies with Erlang Solutions appeared first on Erlang Solutions .

The Ignite Realtime developer community is happy to announce that Smack 4.5 entered its beta phase. Smack is a XMPP client API written in Java that is able to run on Java SE and Android. Smack’s beta phase started already a few weeks ago, but 4.5.0-beta5 is considered to be a good candidate to announce, as many smaller issues have been ironed out.

With Smack 4.5 we bumped the minimum Java version to 11. Furthermore Smack now requires a minimum Android API of 26 to run.

If you are using Smack 4.4 (or maybe an even older version), then right now is the perfect time to create an experimental branch with Smack 4.5 to ease the transition.

Smack 4.5 APIs is considered stable, however small adjustments are still possible during the beta phase.

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Traditionally, the fintech industry relied on proprietary software , with usage and distribution restricted by paid licences. Fintech open-source technologies were distrusted due to security concerns over visible code in complex systems.

But fast-forward to today and financial institutions, including neobanks like Revolut and Monzo, have embraced open source solutions. These banks have built technology stacks on open-source platforms, using new software and innovation to strengthen their competitive edge.

While proprietary software has its role, it faces challenges exemplified by Oracle/Java’s subscription model changes, which have led to significant cost hikes. In contrast, open source Delivers flexibility, scalability, and more control, making it a great choice for fintechs aiming to remain adaptable.

Curious why open source is the smart choice for fintech? Let’s look into how this shift can help future-proof operations, drive innovation, and enhance customer-centric services.

The impact of Oracle Java’s pricing changes

Before we understand why open source is a smart choice for fintech, let’s look at a recent example that highlights the risks of relying on proprietary software—Oracle Java’s subscription model changes.

A change to subscription

Java, known as the “language of business,” has been the top choice for developers and 90% of Fortune 500 companies for over 28 years, due to its stability, performance, and strong Oracle Java community.

In January 2023, Oracle quietly shifted its Java SE subscription model to an employee-based system, charging businesses based on total headcount, not just the number of users. This change alarmed many subscribers and resulted in steep increases in licensing fees. According to Gartner , these changes made operations two to five times more expensive for most organisations.

Oracle Java SE Universal Subscription Global Price List (by volume)

Impact on Oracle Java SE user base

By January 2024, many Oracle Java SE subscribers had switched to OpenJDK, the open-source version of Java. Online sentiment towards Oracle has been unfavourable, with many users expressing dissatisfaction in forums. Those who stuck with Oracle are now facing hefty subscription fee increases with little added benefit.

Lessons from Oracle Java SE

For fintech companies, Oracle Java’s pricing changes have highlighted the risks of proprietary software. In particular, there are unexpected cost hikes, less flexibility, and disruptions to critical infrastructure. Open source solutions, on the other hand, give fintech firms more control, reduce vendor lock-in, and allow them to adapt to future changes while keeping costs in check.

The advantages of open source technologies for Fintech

Open source software is gaining attention in financial institutions, thanks to the rise of digital financial services and fintech advancements.

It is expected to grow by 24% by 2025 and companies that embrace open-source benefit from enhanced security, support for cryptocurrency trading, and a boost to fintech innovation.

Cost-effectiveness

The cost advantages of open-source software have been a major draw for companies looking to shift from proprietary systems. For fintech companies, open-source reduces operational expenses compared to the unpredictable, high costs of proprietary solutions like Oracle Java SE.

Open source software is often free, allowing fintech startups and established firms to lower development costs and redirect funds to key areas such as compliance, security, and user experience. It also avoids fees like:

• Multi-user licences
• Administrative charges
• Ongoing annual software support charges

These savings help reduce operating expenses while enabling investment in valuable services like user training, ongoing support, and customised development, driving growth and efficiency.

A solution to big tech monopolies

Monopolies in tech, particularly in fintech, are increasing. As reported by CB Insights , about 80% of global payment transactions are controlled by just a few major players. These monopolies stifle innovation and drive up costs.

Open-source software decentralises development, preventing any single entity from holding total control. It offers fintech companies an alternative to proprietary systems, reducing reliance on monopolistic players and fostering healthy competition. Open-source models promote transparency, innovation, and lower costs, helping create more inclusive and competitive systems.

Transparent and secure solutions

Security concerns have been a major roadblock that causes companies and startups to hesitate in adopting open-source software.

A common myth about open source is that its public code makes it insecure. But, open-source benefits from transparency, as it allows for continuous public scrutiny. Security flaws are discovered and addressed quickly by the community, unlike proprietary software, where vulnerabilities may remain hidden.

An example is Vocalink , which powers real-time global payment systems. Vocalink uses Erlang , an open-source language designed for high-availability systems, ensuring secure, scalable payment handling. The transparency of open source allows businesses to audit security, ensure compliance, and quickly implement fixes, leading to more secure fintech infrastructure.

Ongoing community support

Beyond security, open source benefits from vibrant communities of developers and users who share knowledge and collaborate to enhance software. This fosters innovation and accelerates development, allowing for faster adaptation to trends or market demands.

Since the code is open, fintech firms can build custom solutions, which can be contributed back to the community for others to use. The rapid pace of innovation within these communities helps keep the software relevant and adaptable.

Interoperability

Interoperability is a game-changer for open-source solutions in financial institutions, allowing for the seamless integration of diverse applications and systems- essential for financial services with complex tech stacks.

By adopting open standards (publicly accessible guidelines ensuring compatibility), financial institutions can eliminate costly manual integrations and enable plug-and-play functionality. This enhances agility, allowing institutions to adopt the best applications without being tied to a single vendor.

A notable example is NatWest’s Backplane , an open-source interoperability solution built on FDC3 standards. Backplane enables customers and fintechs to integrate their desktop apps with various banking and fintech applications, enhancing the financial desktop experience. This approach fosters innovation, saves time and resources, and creates a more flexible, customer-centric ecosystem.

Future-proofing for longevity

Open-source software has long-term viability. Since the source code is accessible, even if the original team disbands, other organisations, developers or the community at large can maintain and update the software. This ensures the software remains usable and up-to-date, preventing reliance on unsupported tools.

Open Source powering Fintech trends

According to the latest study by McKinsey and Company , Artificial Intelligence (AI), machine learning (ML), blockchain technology, and hyper-personalisation will be among some of the key technologies driving financial services in the next decade.

Open-source platforms will play a key role in supporting and accelerating these developments, making them more accessible and innovative.

AI and fintech innovation

• Cost-effective AI/ML : Open-source AI frameworks like TensorFlow , PyTorch , and Scikit -learn enable startups to prototype and deploy AI models affordably, with the flexibility to scale as they grow. This democratisation of AI allows smaller players to compete with larger firms.
• Fraud detection and personalisation : AI-powered fraud detection and personalised services are central to fintech innovation. Open-source AI libraries help companies like Stripe and PayPal detect fraudulent transactions by analysing patterns, while AI enables dynamic pricing and custom loan offers based on user behaviour.
• Efficient operations : AI streamlines back-office tasks through automation, knowledge graphs, and natural language processing (NLP), improving fraud detection and overall operational efficiency.
• Privacy-aware AI : Emerging technologies like federated learning and encryption tools help keep sensitive data secure, for rapid AI innovation while ensuring privacy and compliance.

Blockchain and fintech

Open-source blockchain platforms allow fintech startups to innovate without the hefty cost of proprietary systems:

• Open-source blockchain platforms : Platforms like Ethereum , Bitcoin Core, and Hyperledger are decentralising finance, providing transparency, reducing reliance on intermediaries, and reshaping financial services.
• Decentralised finance (DeFi) :  DeFi is projected to see an impressive rise, with P2P lending growing from $43.16 billion in 2018 to an estimated $567.3 billion by 2026 . Platforms like Uniswap and Aave , built on Ethereum, are pioneering decentralised lending and asset management, offering an alternative to traditional banking. By 2023, Ethereum alone locked $23 billion in DeFi assets, proving its growing influence in the fintech space. Enterprise blockchain solutions: Open source frameworks like Hyperledger Fabric and Corda are enabling enterprises to develop private, permissioned blockchain solutions, enhancing security and scalability across industries, including finance.

Cost-effective innovation: Startups leveraging open-source blockchain technologies can build innovative financial services while keeping costs low, helping them compete effectively with traditional financial institutions.

Hyper-personalisation

Hyper-personalisation is another key trend in fintech, with AI and open-source technologies enabling companies to create highly tailored financial products. This shift moves away from the traditional “one-size-fits-all” model, helping fintechs solve niche customer challenges and deliver more precise services.

Consumer demand for personalisation

A Salesforce survey found that 65% of consumers expect businesses to personalise their services, while 86% are willing to share data to receive more customised experiences.

source- State of the connected customer

The expectation for personalised services is shaping how financial institutions approach customer engagement and product development.

Real-world examples of open-source fintech

Companies like Robinhood and Chime leverage open-source tools to analyse user data and create personalised financial recommendations. These platforms use technologies like Apache Kafka and Apache Spark to process real-time data, improving the accuracy and relevance of their personalised offerings-from customised investment options to tailored loan products.

Implementing hyper-personalisation lets fintech companies strengthen customer relationships, boost retention, and increase deposits. By leveraging real-time, data-driven technologies, they can offer highly relevant products that foster customer loyalty and maximise value throughout the customer lifecycle. With the scalability and flexibility of open-source solutions, companies can provide precise, cost-effective personalised services, positioning themselves for success in a competitive market.

Erlang and Elixir: Open Source solutions for fintech applications

Released as open-source in 1998 , Erlang has become essential for fintech companies that need scalable, high-concurrency, and fault-tolerant systems. Its open-source nature, combined with the capabilities of Elixir (which builds on Erlang’s robust architecture), enables fintech firms to innovate without relying on proprietary software, providing the flexibility to develop custom and efficient solutions.

Both Erlang and Elixir’s architecture are designed to ensure potentially zero downtime, making them well-suited for real-time financial transactions.

Why Erlang and Elixir are ideal for Fintech:

• Reliability : Erlang’s and Elixir’s design ensures that applications continue to function smoothly even during hardware or network failures, crucial for financial services that operate 24/7, guaranteeing uninterrupted service. Elixir inherits Erlang’s reliability while providing a more modern syntax for development.
• Scalability : Erlang and Elixir can handle thousands of concurrent processes, making them perfect for fintech companies looking to scale quickly, especially when dealing with growing data volumes and transactions. Elixir enhances Erlang’s scalability with modern tooling and enhanced performance for certain types of workloads.
• Fault tolerance: Built-in error detection and recovery features ensure that unexpected failures are managed with minimal disruption. This is vital for fintech applications, where downtime can lead to significant financial losses. Erlang’s auto restoration philosophy and Elixir’s features enable 100% availability and no transaction is lost.
• Concurrency & distribution : Both Erlang and Elixir excel at managing multiple concurrent processes across distributed systems. This makes them ideal for fintechs with global operations that require real-time data processing across various locations.

Open-source fintech use cases

Several leading fintech companies have already used Erlang to build scalable, reliable systems that support their complex operations and real-time transactions.

• Klarna : This major European fintech relies on Erlang to manage real-time e-commerce payment solutions, where scalability and reliability are critical for managing millions of transactions daily.
• Goldman Sachs : Erlang is utilised in Goldman Sachs’ high-frequency trading platform, allowing for ultra-low latency and real-time processing essential for responding to market conditions in microseconds.
• Kivra : Erlang/ Elixir supports Kivra’s backend services, managing secure digital communications for millions of users, and ensuring constant uptime and data security.

Erlang and Elixir -supporting future fintech trends

The features of Erlang and Elixir align well with emerging fintech trends:

• DeFi and Decentralised Applications (dApps) : With the growth of decentralised finance (DeFi), Erlang’s and Elixir’s fault tolerance and real-time scalability make them ideal for building dApps that require secure, distributed networks capable of handling large transaction volumes without failure.
• Hyperpersonalisation : As demand for hyperpersonalised financial services grows, Erlang and Elixir’s ability to process vast amounts of real-time data across users simultaneously makes them vital for delivering tailored, data-driven experiences.
• Open banking : Erlang and Elixir’s concurrency support enables fintechs to build seamless, scalable platforms in the open banking era, where various financial systems must interact across multiple applications and services to provide integrated solutions.

Erlang and Elixir can handle thousands of real-time transactions with zero downtime making them well-suited for trends like DeFi, hyperpersonalisation, and open banking. Their flexibility and active developer community ensure that fintechs can innovate without being locked into costly proprietary software.

To conclude

Fintech businesses are navigating an increasingly complex and competitive landscape where traditional solutions no longer provide a competitive edge. If you’re a company still reliant on proprietary software, ask yourself: Is your system equipped to expect the unexpected? Can your existing solutions keep up with market demands?

Open-source technologies offer a solution to these challenges. Fintech firms can reduce costs, improve security, and, most importantly, innovate and scale according to their needs. Whether by reducing vendor lock-ins, tapping into a vibrant developer community, or leveraging customisation, open-source software is set to transform the fintech experience, providing the tools necessary to stay ahead in a digital-first world. If you’re interested in exploring how open-source solutions like Erlang or Elixir can help future-proof your fintech systems, contact the Erlang Solutions team .

The post Why Open Source Technologies is a Smart Choice for Fintech Businesses appeared first on Erlang Solutions .