The best Large Language Models of October 2024

The TIMETOACT GROUP LLM Benchmarks highlight the most powerful AI language models for digital product development. Discover which large language models performed best in october.

We have a few new models to talk about, so let’s get started:

Grok2 from X.AI - Suddenly in TOP 15
Gemini 1.5 Flash 8B - Future Perfect
New Claude Sonnet 3.5 and Haiku 3.5 - Getting better

LLM Benchmarks | Oktober 2024

The benchmarks evaluate the models in terms of their suitability for digital product development. The higher the score, the better.

☁️ - Cloud models with proprietary license
✅ - Open source models that can be run locally without restrictions
🦙 - Local models with Llama license

Can the model generate code and help with programming?

The estimated cost of running the workload. For cloud-based models, we calculate the cost according to the pricing. For on-premises models, we estimate the cost based on GPU requirements for each model, GPU rental cost, model speed, and operational overhead.

How well does the model support work with product catalogs and marketplaces?

How well can the model work with large documents and knowledge bases?

Can the model easily interact with external APIs, services and plugins?

How well can the model support marketing activities, e.g. brainstorming, idea generation and text generation?

How well can the model reason and draw conclusions in a given context?

The "Speed" column indicates the estimated speed of the model in requests per second (without batching). The higher the speed, the better.

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Model	Code	Crm	Docs	Integrate	Marketing	Reason	Final	cost	Speed
1. GPT o1-preview v1/2024-09-12 ☁️	95	92	94	96	88	87	92	52.32 €	0.08 rps
2. GPT o1-mini v1/2024-09-12 ☁️	93	96	94	85	82	87	90	8.15 €	0.16 rps
3. Google Gemini 1.5 Pro v2 ☁️	86	97	94	100	78	74	88	1.00 €	1.18 rps
4. GPT-4o v1/2024-05-13 ☁️	90	96	100	89	78	74	88	1.21 €	1.44 rps
5. GPT-4o v3/dyn-2024-08-13 ☁️	90	97	100	81	79	78	88	1.22 €	1.21 rps
6. GPT-4 Turbo v5/2024-04-09 ☁️	86	99	98	100	88	43	86	2.45 €	0.84 rps
7. GPT-4o v2/2024-08-06 ☁️	90	84	97	92	82	59	84	0.63 €	1.49 rps
8. Google Gemini 1.5 Pro 0801 ☁️	84	92	79	100	70	74	83	0.90 €	0.83 rps
9. Qwen 2.5 72B Instruct ⚠️	79	92	94	100	71	59	83	0.10 €	0.66 rps
10. Llama 3.1 405B Hermes 3🦙	68	93	89	100	88	53	82	0.54 €	0.49 rps
11. Claude 3.5 Sonnet v2 ☁️	82	97	93	85	71	57	81	0.95 €	0.09 rps
12. GPT-4 v1/0314 ☁️	90	88	98	70	88	45	80	7.04 €	1.31 rps
13. X-AI Grok 2 ⚠️	63	93	87	89	88	58	79	1.03 €	0.31 rps
14. GPT-4 v2/0613 ☁️	90	83	95	70	88	45	78	7.04 €	2.16 rps
15. Claude 3 Opus ☁️	69	88	100	78	76	58	78	4.69 €	0.41 rps
16. Claude 3.5 Sonnet v1 ☁️	72	83	89	85	80	58	78	0.94 €	0.09 rps
17. GPT-4 Turbo v4/0125-preview ☁️	66	97	100	85	75	43	78	2.45 €	0.84 rps
18. GPT-4o Mini ☁️	63	87	80	70	100	65	78	0.04 €	1.46 rps
19. Meta Llama3.1 405B Instruct🦙	81	93	92	70	75	48	76	2.39 €	1.16 rps
20. GPT-4 Turbo v3/1106-preview ☁️	66	75	98	70	88	60	76	2.46 €	0.68 rps
21. DeepSeek v2.5 236B ⚠️	57	80	91	78	88	57	75	0.03 €	0.42 rps
22. Google Gemini 1.5 Flash v2 ☁️	64	96	89	75	81	44	75	0.06 €	2.01 rps
23. Google Gemini 1.5 Pro 0409 ☁️	68	97	96	85	75	26	74	0.95 €	0.59 rps
24. Meta Llama 3.1 70B Instruct f16🦙	74	89	90	70	75	48	74	1.79 €	0.90 rps
25. Google Gemini Flash 1.5 8B ☁️	70	93	78	69	76	48	72	0.01 €	1.19 rps
26. GPT-3.5 v2/0613 ☁️	68	81	73	81	81	50	72	0.34 €	1.46 rps
27. Meta Llama 3 70B Instruct🦙	81	83	84	60	81	45	72	0.06 €	0.85 rps
28. Mistral Large 123B v2/2407 ☁️	68	79	68	75	75	70	72	0.86 €	1.02 rps
29. Google Gemini 1.5 Pro 0514 ☁️	73	96	79	100	25	60	72	1.07 €	0.92 rps
30. Google Gemini 1.5 Flash 0514 ☁️	32	97	100	75	72	52	71	0.06 €	1.77 rps
31. Google Gemini 1.0 Pro ☁️	66	86	83	78	88	28	71	0.37 €	1.36 rps
32. Meta Llama 3.2 90B Vision🦙	74	84	87	78	71	32	71	0.23 €	1.10 rps
33. GPT-3.5 v3/1106 ☁️	68	70	71	78	78	58	70	0.24 €	2.33 rps
34. GPT-3.5 v4/0125 ☁️	63	87	71	78	78	43	70	0.12 €	1.43 rps
35. Claude 3.5 Haiku ☁️	52	80	72	70	75	68	70	0.32 €	1.24 rps
36. Qwen1.5 32B Chat f16 ⚠️	70	90	82	78	78	20	69	0.97 €	1.66 rps
37. Cohere Command R+ ☁️	63	80	76	70	70	58	69	0.83 €	1.90 rps
38. Gemma 2 27B IT ⚠️	61	72	87	70	89	32	69	0.07 €	0.90 rps
39. Mistral 7B OpenChat-3.5 v3 0106 f16 ✅	68	87	67	70	88	25	67	0.32 €	3.39 rps
40. Gemma 7B OpenChat-3.5 v3 0106 f16 ✅	63	67	84	60	81	46	67	0.21 €	5.09 rps
41. Meta Llama 3 8B Instruct f16🦙	79	62	68	70	80	41	67	0.32 €	3.33 rps
42. Mistral 7B OpenChat-3.5 v2 1210 f16 ✅	63	73	72	69	88	30	66	0.32 €	3.40 rps
43. Mistral 7B OpenChat-3.5 v1 f16 ✅	58	72	72	70	88	33	65	0.49 €	2.20 rps
44. GPT-3.5-instruct 0914 ☁️	47	92	69	62	88	33	65	0.35 €	2.15 rps
45. GPT-3.5 v1/0301 ☁️	55	82	69	78	82	26	65	0.35 €	4.12 rps
46. Llama 3 8B OpenChat-3.6 20240522 f16 ✅	76	51	76	60	88	38	65	0.28 €	3.79 rps
47. Mistral Nemo 12B v1/2407 ☁️	54	58	51	100	75	49	64	0.03 €	1.22 rps
48. Meta Llama 3.2 11B Vision🦙	70	71	65	70	71	36	64	0.04 €	1.49 rps
49. Starling 7B-alpha f16 ⚠️	58	66	67	70	88	34	64	0.58 €	1.85 rps
50. Qwen 2.5 7B Instruct ⚠️	48	77	80	60	69	47	63	0.07 €	1.25 rps
51. Llama 3 8B Hermes 2 Theta🦙	61	73	74	70	85	16	63	0.05 €	0.55 rps
52. Yi 1.5 34B Chat f16 ⚠️	47	78	70	70	86	26	63	1.18 €	1.37 rps
53. Claude 3 Haiku ☁️	64	69	64	70	75	35	63	0.08 €	0.52 rps
54. Liquid: LFM 40B MoE ⚠️	72	69	65	60	82	24	62	0.00 €	1.45 rps
55. Meta Llama 3.1 8B Instruct f16🦙	57	74	62	70	74	32	61	0.45 €	2.41 rps
56. Qwen2 7B Instruct f32 ⚠️	50	81	81	60	66	31	61	0.46 €	2.36 rps
57. Mistral Small v3/2409 ☁️	43	75	71	75	75	26	61	0.06 €	0.81 rps
58. Claude 3 Sonnet ☁️	72	41	74	70	78	28	61	0.95 €	0.85 rps
59. Mixtral 8x22B API (Instruct) ☁️	53	62	62	100	75	7	60	0.17 €	3.12 rps
60. Mistral Pixtral 12B ✅	53	69	73	60	64	40	60	0.03 €	0.83 rps
61. Codestral Mamba 7B v1 ✅	53	66	51	100	71	17	60	0.30 €	2.82 rps
62. Inflection 3 Productivity ⚠️	46	59	39	70	79	61	59	0.92 €	0.17 rps
63. Anthropic Claude Instant v1.2 ☁️	58	75	65	75	65	16	59	2.10 €	1.49 rps
64. Cohere Command R ☁️	45	66	57	70	84	27	58	0.13 €	2.50 rps
65. Anthropic Claude v2.0 ☁️	63	52	55	60	84	34	58	2.19 €	0.40 rps
66. Qwen1.5 7B Chat f16 ⚠️	56	81	60	50	60	36	57	0.29 €	3.76 rps
67. Mistral Large v1/2402 ☁️	37	49	70	78	84	25	57	0.58 €	2.11 rps
68. Microsoft WizardLM 2 8x22B ⚠️	48	76	79	50	62	22	56	0.13 €	0.70 rps
69. Qwen1.5 14B Chat f16 ⚠️	50	58	51	70	84	22	56	0.36 €	3.03 rps
70. MistralAI Ministral 8B ✅	56	55	41	85	68	30	56	0.02 €	1.02 rps
71. MistralAI Ministral 3B ✅	50	48	39	92	60	41	55	0.01 €	1.02 rps
72. Anthropic Claude v2.1 ☁️	29	58	59	78	75	32	55	2.25 €	0.35 rps
73. Llama2 13B Vicuna-1.5 f16🦙	50	37	55	60	82	37	53	0.99 €	1.09 rps
74. Mistral 7B Instruct v0.1 f16 ☁️	34	71	69	59	62	23	53	0.75 €	1.43 rps
75. Mistral 7B OpenOrca f16 ☁️	54	57	76	25	78	27	53	0.41 €	2.65 rps
76. Meta Llama 3.2 3B🦙	52	71	66	70	44	14	53	0.01 €	1.25 rps
77. Google Recurrent Gemma 9B IT f16 ⚠️	58	27	71	60	56	23	49	0.89 €	1.21 rps
78. Codestral 22B v1 ✅	38	47	44	78	66	13	48	0.06 €	4.03 rps
79. Llama2 13B Hermes f16🦙	50	24	37	74	60	42	48	1.00 €	1.07 rps
80. IBM Granite 34B Code Instruct f16 ☁️	63	49	34	70	57	7	47	1.07 €	1.51 rps
81. Mistral Small v2/2402 ☁️	33	42	45	92	56	8	46	0.06 €	3.21 rps
82. DBRX 132B Instruct ⚠️	43	39	43	77	59	10	45	0.26 €	1.31 rps
83. NVIDIA Llama 3.1 Nemotron 70B Instruct🦙	68	54	25	74	28	21	45	0.09 €	0.53 rps
84. Mistral Medium v1/2312 ☁️	41	43	44	61	62	12	44	0.81 €	0.35 rps
85. Meta Llama 3.2 1B🦙	32	40	33	40	68	51	44	0.02 €	1.69 rps
86. Llama2 13B Puffin f16🦙	37	15	44	70	56	39	43	4.70 €	0.23 rps
87. Mistral Small v1/2312 (Mixtral) ☁️	10	67	63	52	56	8	43	0.06 €	2.21 rps
88. Microsoft WizardLM 2 7B ⚠️	53	34	42	59	53	13	42	0.02 €	0.89 rps
89. Mistral Tiny v1/2312 (7B Instruct v0.2) ☁️	22	47	59	38	62	8	39	0.05 €	2.39 rps
90. Gemma 2 9B IT ⚠️	45	25	47	34	68	13	38	0.02 €	0.88 rps
91. Meta Llama2 13B chat f16🦙	22	38	17	60	75	6	36	0.75 €	1.44 rps
92. Mistral 7B Zephyr-β f16 ✅	37	34	46	59	29	4	35	0.46 €	2.34 rps
93. Meta Llama2 7B chat f16🦙	22	33	20	60	50	18	34	0.56 €	1.93 rps
94. Mistral 7B Notus-v1 f16 ⚠️	10	54	25	52	48	4	32	0.75 €	1.43 rps
95. Orca 2 13B f16 ⚠️	18	22	32	22	67	20	30	0.95 €	1.14 rps
96. Mistral 7B v0.1 f16 ☁️	0	9	48	53	52	12	29	0.87 €	1.23 rps
97. Mistral 7B Instruct v0.2 f16 ☁️	11	30	54	12	58	8	29	0.96 €	1.12 rps
98. Google Gemma 2B IT f16 ⚠️	33	28	16	57	15	20	28	0.30 €	3.54 rps
99. Microsoft Phi 3 Medium 4K Instruct 14B f16 ⚠️	5	34	30	11	47	8	22	0.82 €	1.32 rps
100. Orca 2 7B f16 ⚠️	22	0	26	20	52	4	21	0.78 €	1.38 rps
101. Google Gemma 7B IT f16 ⚠️	0	0	0	9	62	0	12	0.99 €	1.08 rps
102. Meta Llama2 7B f16🦙	0	5	22	3	28	2	10	0.95 €	1.13 rps
103. Yi 1.5 9B Chat f16 ⚠️	0	4	29	9	0	8	8	1.41 €	0.76 rps

Grok 2 Beta from X-AI

This wasn’t expected, but the second version of Grok from X-AI suddenly started making sense (the previous one was nearly useless). Grok 2 Beta made its way into TOP 15. The model performed overall quite well on tasks extracted from LLM products in our benchmark. Even its Reason capability is quite nice.

The model is running close to older versions of GPT-4, but it is still worse than the Qwen 2.5 72B instruct which you can download and run on your own hardware. Nonetheless the news is great. Pretty much any company can make it into the TOP-20 of our benchmark, if they have enough diverse data and access to compute capability for the training.

Gemini 1.5 Flash 8B - Future Perfect

In the LLM Benchmark for September we’ve talked about new models in Llama 3.2 series. They have really pushed state of the art for the local models back then. The progress doesn’t stop there.

Google has released new Gemini 1.5 Flash model. It shows nice results on our product benchmark. This 8B model performs on the level of GPT3.5 or Llama 3 70B, almost catching up with the normal 1.5 Flash.

The model also illustrates the progress of Google in LLM development. This is the cheapest model that ranks quite high compared even to the other Gemini Pro LLMs released in previous months.

The biggest limitation of this model: it is closed. Even though we know its size - 8B, it isn’t possible to download the weights and run things locally.

However, Gemini 1.5 Flash can be used quite cheaply. Plus, as history tells us, whatever one company has achieved, another company could soon repeat. So we’ll be waiting for more small models of this quality, preferably locally-capable.

Claude Sonnet 3.5 and Haiku 3.5 - Getting better

Anthropic released updates to the two models in its lineup:

Medium: Sonnet 3.5
Small: Haiku 3.5

Sonnet 3.5 is currently the highest scoring model from Anthropic in our benchmark. It jumped to 11th place.

Compared to the previous version of Sonnet 3.5, this version shows improved instruction-following and enhanced capabilities with code, both in writing and handling more complex engineering tasks.

Claude 3.5 Sonnet v2 is overall a decent model, but you can get better quality for lower price. For example, by using GPT-4o or running a local Qwen 2.5.

Claude 3.5 Haiku is another improvement in the Haiku series. The model has improved scores across the board (except the Code+Engineering category). The biggest jump was in Reason: from 35 to 68! This is the highest Reason score for all Anthropic models. Could this point towards a new architecture in the next Claude series?

Additional facts to support this theory: Haiku model was the last one to come out, plus it costs 4x times more than the previous Haiku version. Cost structure changes in LLMs are usually aligned with the underlying architectural changes.

Because of the price hike, Haiku is no longer in the “smart and extremely cheap” category. At this point you can find better models like GPT-4o mini or Google Gemini 1.5 Flash 8B.

Overall trend of quality increases within the model ranges - continues. Let’s see if the improved Reason will show up in the other model releases from Anthropic.

Trends

Speaking of the trends, take a look at this interesting meta-trend. OpenAI, Google and Sonnet within last two months have introduced new lower-tier models into higher pricing tiers. This make the charts look as is the LLM performance is actually degrading within these tiers.

This could potentially mean a combination of three things:

LLM Providers are starting to optimise their price offerings based on Cost and usage.
It isn’t anymore possible to compete on quality without starting to increase compute resources (could we be hitting the limits of transformers architecture?)
Our price brackets for categories were not chosen well. We’ll need to redo the entire chart.

And if we plot Gemini 1.5 Flash 8B on the map of locally-capable models, the picture looks like the one below, marking a nice performance jump in the State-of-the-Art.

Let’s see how things continue into November 2024. We will keep you updated!

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TIMETOACT digitizes several test procedures for KI.TEST to determine professional intelligence and personality.

TIMETOACT

Referenz

Standardized data management creates basis for reporting

TIMETOACT implements a higher-level data model in a data warehouse for TRUMPF Photonic Components and provides the necessary data integration connection with Talend. With this standardized data management, TRUMPF will receive reports based on reliable data in the future and can also transfer the model to other departments.

Rinat AbdullinBlog

Blog

Using NLP libraries for post-processing

Learn how to analyse sticky notes in miro from event stormings and how this analysis can be carried out with the help of the spaCy library.

Branche

Artificial Intelligence in Treasury Management

Optimize treasury processes with AI: automated reports, forecasts, and risk management.

Laura GaetanoBlog

Blog

Using a Skill/Will matrix for personal career development

Discover how a Skill/Will Matrix helps employees identify strengths and areas for growth, boosting personal and professional development.

Sebastian BelczykBlog

Blog

Building A Shell Application for Micro Frontends | Part 4

We already have a design system, several micro frontends consuming this design system, and now we need a shell application that imports micro frontends and displays them.

TIMETOACT

Referenz

Flexibility in the data evaluation of a theme park

With the support of TIMETOACT, an theme park in Germany has been using TM1 for many years in different areas of the company to carry out reporting, analysis and planning processes easily and flexibly.

Referenz

Automated Planning of Transport Routes

Efficient transport route planning through automation and seamless integration.

Offering

Offering

Advanced Admin Trial

If you’re interested in Advanced Admin, you can test it for free for 14 days via the Marketplace.

Felix KrauseBlog

Blog

Creating a Cross-Domain Capable ML Pipeline

As classifying images into categories is a ubiquitous task occurring in various domains, a need for a machine learning pipeline which can accommodate for new categories is easy to justify. In particular, common general requirements are to filter out low-quality (blurred, low contrast etc.) images, and to speed up the learning of new categories if image quality is sufficient. In this blog post we compare several image classification models from the transfer learning perspective.

Rinat AbdullinBlog

Blog

State of Fast Feedback in Data Science Projects

DSML projects can be quite different from the software projects: a lot of R&D in a rapidly evolving landscape, working with data, distributions and probabilities instead of code. However, there is one thing in common: iterative development process matters a lot.

Felix KrauseBlog

Blog

Part 2: Detecting Truck Parking Lots on Satellite Images

In the previous blog post, we created an already pretty powerful image segmentation model in order to detect the shape of truck parking lots on satellite images. However, we will now try to run the code on new hardware and get even better as well as more robust results.

Felix KrauseBlog

Blog

Part 1: Detecting Truck Parking Lots on Satellite Images

Real-time truck tracking is crucial in logistics: to enable accurate planning and provide reliable estimation of delivery times, operators build detailed profiles of loading stations, providing expected durations of truck loading and unloading, as well as resting times. Yet, how to derive an exact truck status based on mere GPS signals?

Laura GaetanoBlog

Blog

5 lessons from running a (remote) design systems book club

Last year I gifted a design systems book I had been reading to a friend and she suggested starting a mini book club so that she’d have some accountability to finish reading the book. I took her up on the offer and so in late spring, our design systems book club was born. But how can you make the meetings fun and engaging even though you're physically separated? Here are a couple of things I learned from running my very first remote book club with my friend!

Ian RussellBlog

Blog

Introduction to Functional Programming in F# – Part 2

Explore functions, types, and modules in F#. Enhance your skills with practical examples and insights in this detailed guide.

Rinat AbdullinBlog

Blog

Machine Learning Pipelines

In this first part, we explain the basics of machine learning pipelines and showcase what they could look like in simple form. Learn about the differences between software development and machine learning as well as which common problems you can tackle with them.

Rinat AbdullinBlog

Blog

Event Sourcing with Apache Kafka

For a long time, there was a consensus that Kafka and Event Sourcing are not compatible with each other. So it might look like there is no way of working with Event Sourcing. But there is if certain requirements are met.

Felix KrauseBlog

Blog

Boosting speed of scikit-learn regression algorithms

The purpose of this blog post is to investigate the performance and prediction speed behavior of popular regression algorithms, i.e. models that predict numerical values based on a set of input variables.

Chrystal LantnikBlog

Blog

CSS :has() & Responsive Design

In my journey to tackle a responsive layout problem, I stumbled upon the remarkable benefits of the :has() pseudo-class. Initially, I attempted various other methods to resolve the issue, but ultimately, embracing the power of :has() proved to be the optimal solution. This blog explores my experience and highlights the advantages of utilizing the :has() pseudo-class in achieving flexible layouts.

Daniel PuchnerBlog

Blog

How we discover and organise domains in an existing product

Software companies and consultants like to flex their Domain Driven Design (DDD) muscles by throwing around terms like Domain, Subdomain and Bounded Context. But what lies behind these buzzwords, and how these apply to customers' diverse environments and needs, are often not as clear. As it turns out it takes a collaborative effort between stakeholders and development team(s) over a longer period of time on a regular basis to get them right.

Christian FolieBlog

Blog

Running Hybrid Workshops

When modernizing or building systems, one major challenge is finding out what to build. In Pre-Covid times on-site workshops were a main source to get an idea about ‘the right thing’. But during Covid everybody got used to working remotely, so now the question can be raised: Is it still worth having on-site, physical workshops?

Ian RussellBlog

Blog

Introduction to Functional Programming in F# – Part 3

Dive into F# data structures and pattern matching. Simplify code and enhance functionality with these powerful features.

Ian RussellBlog

Blog

Introduction to Functional Programming in F# – Part 4

Unlock F# collections and pipelines. Manage data efficiently and streamline your functional programming workflow with these powerful tools.

Ian RussellBlog

Blog

So, I wrote a book

Join me as I share the story of writing a book on F#. Discover the challenges, insights, and triumphs along the way.

Nina DemuthBlog

Blog

7 Positive effects of visualizing the interests of your team

Interests maps unleash hidden potentials and interests, but they also make it clear which topics are not of interest to your colleagues.

Daniel PuchnerBlog

Blog

How to gather data from Miro

Learn how to gather data from Miro boards with this step-by-step guide. Streamline your data collection for deeper insights.

Christian FolieBlog

Blog

Designing and Running a Workshop series: An outline

Learn how to design and execute impactful workshops. Discover tips, strategies, and a step-by-step outline for a successful workshop series.

Christian FolieBlog

Blog

Designing and Running a Workshop series: The board

In this part, we discuss the basic design of the Miro board, which will aid in conducting the workshops.

Sebastian BelczykBlog

Blog

Composite UI with Design System and Micro Frontends

Discover how to create scalable composite UIs using design systems and micro-frontends. Enhance consistency and agility in your development process.

Sebastian BelczykBlog

Blog

Building and Publishing Design Systems | Part 2

Learn how to build and publish design systems effectively. Discover best practices for creating reusable components and enhancing UI consistency.

Sebastian BelczykBlog

Blog

Building a micro frontend consuming a design system | Part 3

In this blopgpost, you will learn how to create a react application that consumes a design system.

Ian RussellBlog

Blog

Introduction to Functional Programming in F# – Part 5

Master F# asynchronous workflows and parallelism. Enhance application performance with advanced functional programming techniques.

Daniel PuchnerBlog

Blog

Make Your Value Stream Visible Through Structured Logging

Boost your value stream visibility with structured logging. Improve traceability and streamline processes in your software development lifecycle.

Aqeel AlazreeBlog

Blog

Part 2: Data Analysis with powerful Python

Analyzing and visualizing data from a SQLite database in Python can be a powerful way to gain insights and present your findings. In Part 2 of this blog series, we will walk you through the steps to retrieve data from a SQLite database file named gold.db and display it in the form of a chart using Python. We'll use some essential tools and libraries for this task.

Ian RussellBlog

Blog

Introduction to Functional Programming in F#

Dive into functional programming with F# in our introductory series. Learn how to solve real business problems using F#'s functional programming features. This first part covers setting up your environment, basic F# syntax, and implementing a simple use case. Perfect for developers looking to enhance their skills in functional programming.

Ian RussellBlog

Blog

Introduction to Partial Function Application in F#

Partial Function Application is one of the core functional programming concepts that everyone should understand as it is widely used in most F# codebases.In this post I will introduce you to the grace and power of partial application. We will start with tupled arguments that most devs will recognise and then move onto curried arguments that allow us to use partial application.

Rinat AbdullinBlog

Blog

Inbox helps to clear the mind

I hate distractions. They can easily ruin my day when I'm in the middle of working on a cool project. They do that by overloading my mind, buzzing around inside me, and just making me tired. Even though we can think about several things at once, we can only do one thing at a time.

Ian RussellBlog

Blog

Creating solutions and projects in VS code

In this post we are going to create a new Solution containing an F# console project and a test project using the dotnet CLI in Visual Studio Code.

Ian RussellBlog

Blog

Using Discriminated Union Labelled Fields

A few weeks ago, I re-discovered labelled fields in discriminated unions. Despite the fact that they look like tuples, they are not.

Ian RussellBlog

Blog

Ways of Creating Single Case Discriminated Unions in F#

There are quite a few ways of creating single case discriminated unions in F# and this makes them popular for wrapping primitives. In this post, I will go through a number of the approaches that I have seen.

Rinat AbdullinBlog

Blog

Part 1: TIMETOACT Logistics Hackathon - Behind the Scenes

A look behind the scenes of our Hackathon on Sustainable Logistic Simulation in May 2022. This was a hybrid event, running on-site in Vienna and remotely. Participants from 12 countries developed smart agents to control cargo delivery truck fleets in a simulated Europe.

Bernhard SchauerBlog

Blog

Isolating legacy code with ArchUnit tests

Clear boundaries in code are important ... and hard. ArchUnit allows you to capture the structure your team agreed on in tests.

Jonathan ChannonBlog

Blog

Understanding F# applicatives and custom operators

In this post, Jonathan Channon, a newcomer to F#, discusses how he learnt about a slightly more advanced functional concept — Applicatives.

Nina DemuthBlog

Blog

From the idea to the product: The genesis of Skwill

We strongly believe in the benefits of continuous learning at work; this has led us to developing products that we also enjoy using ourselves. Meet Skwill.

Ian RussellBlog

Blog

Introduction to Functional Programming in F# – Part 10

Discover Agents and Mailboxes in F#. Build responsive applications using these powerful concurrency tools in functional programming.

Rinat AbdullinBlog

Blog

Process Pipelines

Discover how process pipelines break down complex tasks into manageable steps, optimizing workflows and improving efficiency using Kanban boards.

Ian RussellBlog

Blog

Introduction to Functional Programming in F# – Part 11

Learn type inference and generic functions in F#. Boost efficiency and flexibility in your code with these essential programming concepts.

Rinat AbdullinBlog

Blog

Innovation Incubator at TIMETOACT GROUP Austria

Discover how our Innovation Incubator empowers teams to innovate with collaborative, week-long experiments, driving company-wide creativity and progress.

Rinat AbdullinBlog