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Is Your AI Still a Pilot? Here’s How Enterprises Cross the Finish Line

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Generative AI in the Real World: Lessons From Early Enterprise Winners

Evangelos Simoudis occupies a valuable vantage point at the intersection of AI innovation and enterprise adoption. Because he engages directly with both corporations navigating AI implementation and the startups building new solutions, I always appreciate checking in with him. His insights are grounded in a unique triangulation of data streams, including firsthand information from his AI-focused portfolio companies and their clients, confidential advisory work with large corporations, and discussions with market analysts. Below is a heavily edited excerpt from our recent conversation about the current state of AI adoption.

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Current State of AI Adoption

What is the current state of AI adoption in enterprises, particularly regarding generative AI versus traditional AI approaches?

There’s growing interest in AI broadly, but it’s important to distinguish between generative AI and discriminative AI (also called traditional AI). Discriminative AI adoption is progressing well, with many experimental projects now moving to deployment with allocated budgets.

For generative AI, there’s still a lot of experimentation happening, but fewer projects are moving from POCs to actual deployment. We expect more generative AI projects to move toward deployment by the end of the year, but we’re still in the hype stage rather than broad adoption.

As for agentic systems, we’re seeing even fewer pilots. Enterprises face a “bandwidth bottleneck” similar to what we see in cybersecurity – there are so many AI systems being presented to executives that they only have limited capacity to evaluate them all.

In which business functions are generative AI projects successfully moving from pilots to production?

Three major use cases stand out:

  1. Customer support – various types of customer support applications where generative AI can enhance service
  2. Programming functions – automating software production, testing, and related development activities
  3. Intelligent documents – using generative AI to automate form-filling or extract data from documents

These three areas are where we see the most significant movement from experimentation to production, both in solutions from private companies and internal corporate efforts.

Which industries are leading the adoption of generative AI?

Financial services and technology-driven companies are at the forefront. For example:

  • Intuit is applying generative AI for customer support with measurable improvements in customer satisfaction and productivity, reporting 4-5× developer-productivity gains
  • JP Morgan and Morgan Stanley are seeing productivity improvements in their private client divisions, where associates can prepare for client meetings more efficiently by using generative AI to compile and summarize research
  • ServiceNow is having success in IT customer support, reporting over $10 million in revenue directly attributed to AI implementations and dramatic improvements in handling problem tickets more efficiently

Interestingly, automotive is not among the leading industries in generative AI adoption. They’re facing more immediate challenges like tariff issues that are taking priority over AI initiatives.

Keys to Successful Implementation

What are the key characteristics of companies that successfully move from AI experimentation to production?

Three main characteristics stand out:

  1. They are long-term experimenters. These companies haven’t just jumped into AI in the last few months. They’ve been experimenting for years with significant funding and resources, both human and financial.
  2. They are early technology adopters. These organizations have been monitoring the evolution of large language models, understanding the differences between versions, and making informed decisions about which models to use (open vs. closed, etc.). Importantly, they have the right talent who can make these assessments.
  3. They are willing to change business processes. Perhaps the most expensive and challenging characteristic is their willingness to either incorporate AI into existing business processes or completely redesign processes to be AI-first. This willingness to change processes is perhaps the biggest differentiator between companies that successfully deploy AI and those that remain in the experimental phase.

A good example is Klarna (the financial services company from Sweden), which initially tried using AI-only customer support but had to modify their approach after discovering issues with customer experience. What’s notable is both their initial willingness to completely change their business process and their flexibility to adjust when the original approach didn’t work optimally.

How important is data strategy when implementing generative AI, and what mistakes do companies make?

Data strategy is critically important but often underestimated. One of the biggest mistakes companies make is assuming they can simply point generative AI at their existing data without making changes to their data strategy or platform.

When implementing generative AI, companies need to understand what they’re trying to accomplish. Different approaches – whether using off-the-shelf closed models, fine-tuning open-source models, or building their own language models – each require an associated data strategy. This means not only having the appropriate type of data but also performing the appropriate pre-processing.

Unfortunately, this necessity isn’t always well communicated by vendors to their clients, leading to confusion and resistance. Many executives push back when told they need to reconfigure, clean, or label their data beyond what they’ve already done.

Model Selection & Operational Considerations

How should companies approach AI model selection, particularly regarding open weights versus proprietary models?

There’s significant confusion about what models companies need. Key considerations include:

  • Do you need a single model or multiple models for your application?
  • How much fine-tuning is required?
  • Do you need the largest model, or can you get by with a smaller one?
  • Do you need specialized capabilities like reasoning?

The pace at which new models are released adds to this confusion. The hyperscalers (large cloud providers like Microsoft Azure, Google Cloud, AWS) are making strong inroads as one-stop solutions.

Regarding open weights versus proprietary models, the decision depends on what you’re trying to accomplish, along with considerations of cost, latency, and the talent you have available. The ideal strategy is to architect your application to be model-agnostic or even use multiple models.

There are also concerns about using models from certain geographies, such as Chinese models, due to security considerations, but this is just one factor in the complex decision-making process.

For large corporations already on the cloud, what seems to be the easiest path for sourcing generative AI models and solutions?

The typical hierarchy seems to be:

  1. Hyperscalers: (Microsoft Azure, Google Cloud, AWS) are often the first stop, leveraging existing relationships and infrastructure
  2. Application Companies: (ServiceNow, Salesforce, Databricks) who embed AI into their existing enterprise applications
  3. Pure-Play AI Vendors: (OpenAI, Anthropic) both large and small
  4. Management Consulting Firms: (Accenture, IBM, KPMG)

Enterprises are weighing whether to pursue a best-of-breed strategy or an all-in-one solution, and hyperscalers are making strong inroads offering the latter, integrating various capabilities including risk detection.

How do operational considerations affect AI adoption?

The lack of robust tooling around ML Ops (Machine Learning operations) and LLM Ops (Large Language Model operations) is one reason why many companies struggle to move from experimentation to production.

We’re seeing strong interest in the continuum between data ops, model ops (including ML ops and LLM ops), and DevOps. The hyperscalers don’t have the strongest solutions for these operational challenges, creating an opportunity for startups.

Are there common architectural patterns emerging for production generative AI systems?

Retrieval-Augmented Generation (RAG) is definitely the dominant pattern moving into production. Corporations seem most comfortable with it, likely because it requires the least amount of fundamental change and investment compared to fine-tuning or building models from scratch.

Regarding knowledge graphs and neuro-symbolic systems (combining neural networks with symbolic reasoning, often via graphs), we see the underlying technologies becoming more important in system architecture. However, we’re not seeing significant inbound demand for GraphRAG and graph-based solutions from corporations yet; it’s more of an educational effort currently. Palantir is another company notably pushing a knowledge graph-based approach.

Agentic Systems & Future Outlook

What’s the state of adoption for agentic systems, and what can we expect in the near future?

Currently, we’re seeing individuals working with at most one agent (often called a co-pilot). However, there’s confusion about terminology – we need to distinguish between chatbots, co-pilots, and true agents.

A true agent needs reasoning ability, memory, the ability to learn, perceive the environment, reason about it, remember past actions, and learn from experiences. Most systems promoted as agents today don’t have all these capabilities.

What we have today is mostly single human-single agent interactions. The progression would be to single human-multiple agents before we can advance to multiple agents interacting among themselves. While there’s interest and experimentation with agents, I haven’t seen examples of true agents working independently that enterprises can rely on.

What’s your six-to-twelve-month outlook for enterprise generative AI and agents?

In the next 6-12 months, I expect to see more generative AI applications moving to production across more industries, starting with the three primary use cases mentioned earlier (customer support, programming, intelligent documents).

Success will be judged on CFO-friendly metrics: productivity lift, cost reduction, higher customer satisfaction, and revenue generation. If these implementations prove successful with measurable business impacts, then moving toward agent-driven systems will become easier.

However, a major concern is that the pace of adoption might not be as fast as technology providers hope. The willingness and ability of organizations to change their underlying business processes remains a significant hurdle.

Autonomous Vehicles Case Study

What’s your perspective on camera-only versus multi-sensor approaches for self-driving cars?

I don’t believe in camera-only systems for self-driving cars. While camera-only systems might work in certain idealized environments without rain or fog, deploying one platform across a variety of complex environments with different weather conditions requires a multi-sensor approach (including LiDAR, radar, cameras).

The cost of sensors is decreasing, making it more feasible for companies to incorporate multiple sensors. The key question is determining the optimal number of each type of sensor needed to operate safely in various environments. Fleet operators like Waymo or Zoox have an advantage here because they work with a single type of vehicle with defined geometry and sensor stack.

How important is teleoperations for autonomous vehicles?

Teleoperations are a critical, yet often undiscussed, aspect of current autonomous vehicle deployments. What’s not widely discussed is the ratio of teleoperators to vehicles, which significantly impacts the economics of these systems. Having one teleoperator per 40 vehicles is very different from having one per four vehicles.

Until there’s transparency around these numbers, it’s very difficult to accurately assess which companies have the most efficient and scalable autonomous driving systems. In essence, many current autonomous vehicle systems are multi-agent systems with humans in the loop.

 

The post Is Your AI Still a Pilot? Here’s How Enterprises Cross the Finish Line appeared first on Gradient Flow.

The Model Reliability Paradox: When Smarter AI Becomes Less Trustworthy

The Model Reliability Paradox: When Smarter AI Becomes Less Trustworthy

A curious challenge is emerging from the cutting edge of artificial intelligence. As developers strive to imbue Large Language Models (LLMs) with more sophisticated reasoning capabilities—enabling them to plan, strategize, and untangle complex, multi-step problems—they are increasingly encountering a counterintuitive snag. Models engineered for advanced thinking frequently exhibit higher rates of hallucination and struggle with factual reliability more than their simpler predecessors. This presents developers with a fundamental trade-off, a kind of ‘Model Reliability Paradox’, where the push for greater cognitive prowess appears to inadvertently compromise the model’s grip on factual accuracy and overall trustworthiness.

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This paradox is illustrated by recent evaluations of OpenAI’s frontier language model, o3, which have revealed a troubling propensity for fabricating technical actions and outputs. Research conducted by Transluce found the model consistently generates elaborate fictional scenarios—claiming to execute code, analyze data, and even perform computations on external devices—despite lacking such capabilities. More concerning is the model’s tendency to double down on these fabrications when challenged, constructing detailed technical justifications for discrepancies rather than acknowledging its limitations. This phenomenon appears systematically more prevalent in o-series models compared to their GPT counterparts.

Such fabrications go far beyond simple factual errors. Advanced models can exhibit sophisticated forms of hallucination that are particularly insidious because of their plausibility. These range from inventing non-existent citations and technical details to constructing coherent but entirely false justifications for their claims, even asserting they have performed actions impossible within their operational constraints.

(click to enlarge)

Understanding this Model Reliability Paradox requires examining the underlying mechanics. The very structure of complex, multi-step reasoning inherently introduces more potential points of failure, allowing errors to compound. This is often exacerbated by current training techniques which can inadvertently incentivize models to generate confident or elaborate responses, even when uncertain, rather than admitting knowledge gaps. Such tendencies are further reinforced by training data that typically lacks examples of expressing ignorance, leading models to “fill in the blanks” and ultimately make a higher volume of assertions—both correct and incorrect.

(click to enlarge)

How should AI development teams proceed in the face of the Model Reliability Paradox? I’d start by monitoring progress in foundational models. The onus is partly on the creators of these large systems to address the core issues identified. Promising research avenues offer potential paths forward, focusing on developing alignment techniques that better balance reasoning prowess with factual grounding, equipping models with more robust mechanisms for self-correction and identifying internal inconsistencies, and improving their ability to recognise and communicate the limits of their knowledge. Ultimately, overcoming the paradox will likely demand joint optimization—training and evaluating models on both sophisticated reasoning and factual accuracy concurrently, rather than treating them as separate objectives.

In the interim, as foundation model providers work towards more inherently robust models, AI teams must focus on practical, implementable measures to safeguard their applications. While approaches will vary based on the specific application and risk tolerance, several concrete measures are emerging as essential components of a robust deployment strategy:

  • Define and Scope the Operational Domain. Clearly delineate the knowledge boundaries within which the model is expected to operate reliably. Where possible, ground the model’s outputs in curated, up-to-date information using techniques like RAG and GraphRAG to provide verifiable context and reduce reliance on the model’s potentially flawed internal knowledge.
  • Benchmark Beyond Standard Metrics. Evaluate candidate models rigorously, using not only reasoning benchmarks relevant to the intended task but also specific tests designed to probe for hallucinations. This might include established benchmarks like HaluEval or custom, domain-specific assessments tailored to the application’s critical knowledge areas.
  • Implement Layered Technical Safeguards. Recognise that no single technique is a silver bullet. Combine multiple approaches, such as using RAG for grounding, implementing uncertainty quantification to flag low-confidence outputs, employing self-consistency checks (e.g., generating multiple reasoning paths and checking for consensus), and potentially adding rule-based filters or external verification APIs for critical outputs.
  • Establish Robust Human-in-the-Loop Processes. For high-stakes decisions or when model outputs exhibit low confidence or inconsistencies, ensure a well-defined process for human review and correction. Systematically log failures, edge cases, and corrections to create a feedback loop for refining prompts, fine-tuning models, or improving safeguards.
  • Continuously Monitor and Maintain. Track key performance indicators, including hallucination rates and task success metrics, in production. Model behaviour can drift over time, necessitating ongoing monitoring and periodic recalibration or retraining to maintain acceptable reliability levels.

The post The Model Reliability Paradox: When Smarter AI Becomes Less Trustworthy appeared first on Gradient Flow.

The troubling trade-off every AI team needs to know about

Subscribe • Previous Issues

The Model Reliability Paradox: When Smarter AI Becomes Less Trustworthy

A curious challenge is emerging from the cutting edge of artificial intelligence. As developers strive to imbue Large Language Models (LLMs) with more sophisticated reasoning capabilities—enabling them to plan, strategize, and untangle complex, multi-step problems—they are increasingly encountering a counterintuitive snag. Models engineered for advanced thinking frequently exhibit higher rates of hallucination and struggle with factual reliability more than their simpler predecessors. This presents developers with a fundamental trade-off, a kind of ‘Model Reliability Paradox’, where the push for greater cognitive prowess appears to inadvertently compromise the model’s grip on factual accuracy and overall trustworthiness.

Power Our Content: Upgrade to Premium! ⚡

This paradox is illustrated by recent evaluations of OpenAI’s frontier language model, o3, which have revealed a troubling propensity for fabricating technical actions and outputs. Research conducted by Transluce found the model consistently generates elaborate fictional scenarios—claiming to execute code, analyze data, and even perform computations on external devices—despite lacking such capabilities. More concerning is the model’s tendency to double down on these fabrications when challenged, constructing detailed technical justifications for discrepancies rather than acknowledging its limitations. This phenomenon appears systematically more prevalent in o-series models compared to their GPT counterparts.

Such fabrications go far beyond simple factual errors. Advanced models can exhibit sophisticated forms of hallucination that are particularly insidious because of their plausibility. These range from inventing non-existent citations and technical details to constructing coherent but entirely false justifications for their claims, even asserting they have performed actions impossible within their operational constraints.

(click to enlarge)

Understanding this Model Reliability Paradox requires examining the underlying mechanics. The very structure of complex, multi-step reasoning inherently introduces more potential points of failure, allowing errors to compound. This is often exacerbated by current training techniques which can inadvertently incentivize models to generate confident or elaborate responses, even when uncertain, rather than admitting knowledge gaps. Such tendencies are further reinforced by training data that typically lacks examples of expressing ignorance, leading models to “fill in the blanks” and ultimately make a higher volume of assertions—both correct and incorrect.

(click to enlarge)

How should AI development teams proceed in the face of the Model Reliability Paradox? I’d start by monitoring progress in foundational models. The onus is partly on the creators of these large systems to address the core issues identified. Promising research avenues offer potential paths forward, focusing on developing alignment techniques that better balance reasoning prowess with factual grounding, equipping models with more robust mechanisms for self-correction and identifying internal inconsistencies, and improving their ability to recognise and communicate the limits of their knowledge. Ultimately, overcoming the paradox will likely demand joint optimization—training and evaluating models on both sophisticated reasoning and factual accuracy concurrently, rather than treating them as separate objectives.

In the interim, as foundation model providers work towards more inherently robust models, AI teams must focus on practical, implementable measures to safeguard their applications. While approaches will vary based on the specific application and risk tolerance, several concrete measures are emerging as essential components of a robust deployment strategy:

  • Define and Scope the Operational Domain. Clearly delineate the knowledge boundaries within which the model is expected to operate reliably. Where possible, ground the model’s outputs in curated, up-to-date information using techniques like RAG and GraphRAG to provide verifiable context and reduce reliance on the model’s potentially flawed internal knowledge.
  • Benchmark Beyond Standard Metrics. Evaluate candidate models rigorously, using not only reasoning benchmarks relevant to the intended task but also specific tests designed to probe for hallucinations. This might include established benchmarks like HaluEval or custom, domain-specific assessments tailored to the application’s critical knowledge areas.
  • Implement Layered Technical Safeguards. Recognise that no single technique is a silver bullet. Combine multiple approaches, such as using RAG for grounding, implementing uncertainty quantification to flag low-confidence outputs, employing self-consistency checks (e.g., generating multiple reasoning paths and checking for consensus), and potentially adding rule-based filters or external verification APIs for critical outputs.
  • Establish Robust Human-in-the-Loop Processes. For high-stakes decisions or when model outputs exhibit low confidence or inconsistencies, ensure a well-defined process for human review and correction. Systematically log failures, edge cases, and corrections to create a feedback loop for refining prompts, fine-tuning models, or improving safeguards.
  • Continuously Monitor and Maintain. Track key performance indicators, including hallucination rates and task success metrics, in production. Model behaviour can drift over time, necessitating ongoing monitoring and periodic recalibration or retraining to maintain acceptable reliability levels.

Data Exchange Podcast

1. Vibe Coding and the Rise of AI Agents: The Future of Software Development is Here. Steve Yegge, evangelist at Sourcegraph, explores how “vibe coding” and AI agents are revolutionizing software development by shifting developers from writing code to orchestrating AI systems. The discussion highlights both the dramatic productivity gains possible and the challenges developers face in adapting to this new paradigm.

2. Beyond the Demo: Building AI Systems That Actually Work. In this episode, Hamel Husain, founder of Parlance Labs, discusses how successful AI implementation requires fundamental data science skills and systematic data analysis often overlooked in current educational resources. He emphasizes the importance of involving domain experts and establishing robust evaluation processes based on actual failure modes rather than generic metrics.

The post The troubling trade-off every AI team needs to know about appeared first on Gradient Flow.

Frap Tools to take on high-end keyboard synthesizer market with ‘West Coast’ MAGNOLIA

Frap Tools, a manufacturer of Eurorack modules and accessories alongside professional audio compressors in the 500-Series format, latest venture into the world of high-end keyboard synthesizers with its introduction of MAGNOLIA — announced as an 8-VOICE ANALOG THRU-ZERO FM SYNTHESIZER built to unlock every sound associated with ‘West Coast’ modular synthesis through a classic keyboard interface.

As an 8-VOICE ANALOG THRU-ZERO FM SYNTHESIZER, MAGNOLIA unlocks every sound associated with the so-called ‘West Coast’ modular synthesis world with waveshapers, wavefolders, and, most importantly, analog linear TZFM(Through-Zero Frequency Modulation) Indeed, its intentions are made clear from the outset by the on point wording worked tastefully into its (initial) top panel design. No need to necessarily remain there, though, for fat resonant filters and powerful analogue oscillators also allow for more traditional subtractive synthesis sounds. And although a deep and flexible modulation section encourages complex patches with the push of a button and twist of a knob, switching modulations on and off with the TOGGLE function, it is always easy to keep track of what is going on thanks to the inclusion of LEDs (Light-Emitting Diodes) on every source and destination.

Digging deeper, MAGNOLIA features eight analogue voices with two oscillators — carrier and modulator — and 24 dB/oct resonant HIGH-PASS and LOW-PASS filters; the carrier oscillator has a through-zero core for precise, sideband-rich analogue FM (Frequency Modulation) sounds, so crystal-like pads to growling basses are available to all with the greatest of ease, while FM can be applied to the filters. Furthermore, sculpting sounds that have not been heard before on analogue polysynths are now perfectly possible, thanks to MAGNOLIA’s unique signal flow — think continuously variable waveform shapes and a wavefolder circuit per voice!

Those two oscillators are derived from Frap Tools’ BRENSO (https://frap.tools/products/brenso/), a Euroack module readily representing its creator’s primary analogue source of articulated waveforms whose degree of entanglement can be precisely set by the musician. MAGNOLIA’s Oscillator 1 is the ‘West Coast’ one with TZFM, wavefolder, and FLIP SYNC, while Oscillator 2 is the ‘East Coast’ one, with PWM (Pulse Width Modulation) and fine-tune. Whether wanting experimental FM sounds or punchy synth brass, they are always at anyone’s service. Speaking of Frap Tools’ unique modular soul, MAGNOLIA’s filter section is derived from CUNSA (https://frap.tools/products/cunsa/) — itself a quadruple analogue ping-able multimode resonant filter, saturator, mixer, and oscillator, no less!

Musically, MAGNOLIA sports a keyboard from fellow Italians FATAR, with polyphonic aftertouch capabilities for enhanced expressiveness, while a per-part ARPEGGIATOR and per-part 16-step SEQ (sequencer) serve to creatively complement the synthesizer still further. Features briefly worth drawing attention to here by way of ending on some more high notes include MAGNOLIA’s bi-timbral programs (with SINGLE, MORPH, DUAL, and SPLIT MODES); all-analogue signal path; three loop-able DAHDSR (DELAY, ATTACK, HOLD, DECAY, SUSTAIN, and RELEASE) envelopes; three digital LFOs (Low Frequency Oscillators); and 512 preset memory slots.

Anyone attending SUPERBOOTH25, May 8-10, FEZ-Berlin, Germany can get up close and personal with two MAGNOLIA pre-production units by swinging by Booth B049 there. Their creatorswill be showcasing them personally, eager to show the results of their hard work, while providing a passionate warm welcome. Frap Tools aim to release MAGNOLIA as a production product by the end of summer, albeit with some differences evident by then. The interface will likely be more refined, featuring digital effects, while some features currently under evaluation could conceivably be removed.

While Frap Tools’ Eurorack modules and accessories, plus professional audio compressors in the 500-Series format, are available to purchase online directly from its website (https://frap.tools/), 

The post Frap Tools to take on high-end keyboard synthesizer market with ‘West Coast’ MAGNOLIA appeared first on Decoded Magazine.

Serato & Roland announce SP-404MKII integration with Serato DJ + Serato Studio

oday, in celebration of the 20th anniversary of the iconic SP-404 sampler, Serato and Roland have announced the official integration of the SP-404MKII with Serato DJ and Serato Studio. Roland’s free V5 update with Serato transforms the SP-404MKII into a powerful, accessible tool that bridges the gap between DJing and music production, maximising creativity for live performance while delivering powerful features for sampling and beatmaking. 

The dynamic collaboration between two legendary music brands opens up the seminal 404 beat culture to more DJs and producers than ever before. Furthermore, Serato’s support of Roland’s SP-404MKII promotes new sonic possibilities for performance, sampling and beatmaking through the fusion of Serato’s advanced audio processing and the SP-404MKII’s beloved effects. 

Roland SP-404MKII for Serato DJ: 

  • The new built-in integration with Serato DJ unlocks fresh live performance opportunities with Roland’s compact, customizable and iconic sampler. 
  • As the first DJ software to officially support the SP-404MKII, Serato DJ offers access to a portable performance machine without the complexity of a full DJ set up. 
  • Now with pre-mapped Serato controls, DJs can easily integrate their workflow and perform sets on the go with a versatile, portable DJ rig that has access to Serato Stems, looping, hot cues and much more. 
  • Enjoy smooth transitions, real-time mixing and looping, and access to both SP and Serato FX. 

Roland SP-404MKII for Serato Studio:

  • Roland and Serato’s integration advances the SP-404MKII hardware into a hands-on, pre-mapped controller and USB audio interface for Serato Studio with even deeper hardware integration. 
  • Producers now have the ability to route Serato Studio audio through the SP-404MKII’s onboard effects for real-time processing. 
  • Separate stems and trigger loops and one-shots directly in Serato Studio with the famed hardware’s 16 performance pads. 
  • With Roland’s iconic SP-404MKII, producers can utilize a portable recording studio on-the-go while tapping into Serato’s powerful software features. 

Explore integrated functionality with the SP-404MKII V5 update for Serato DJ Lite, Serato DJ Pro* and Serato Studio. *Requires a DJ Pro license. 

Serato’s official support for Roland’s internationally-renowned portable sampler launches alongside a string of global events in the vibrant cities that were vital hubs of the early 404 beat community, from Los Angeles, Atlanta, Tokyo, Berlin, London and beyond. 

Live performances, beat battles, artist panels and exclusive merchandise giveaways will unite the artists, DJs and producers who were integral to the cultural phenomenon that is the SP-404. Roland stores around the world will host in-person celebrations – from London to Tokyo – where guests can join 404 Day gear raffles for creative tools aligned with the SP-404MKII, including Serato licenses, the AIRA Compact S-1 and J-6, a limited-edition Roland graphic tee, Roland Cloud access, Melodics subscriptions and more. To join this landmark 20th year celebration of the SP-404, visit Roland’s dedicated 404 Day page here and Roland’s ultimate guide for 404 Day 2025 here. 

The post Serato & Roland announce SP-404MKII integration with Serato DJ + Serato Studio appeared first on Decoded Magazine.

WATCH: Take a look at Aphex Twin’s Theis Modular Synthesizer

It’s not often one gets their hands on a piece of music history, but that is what music buff Alex Ball recently did when he purchased Apex Twin’s Theis Modular Synthesizer and decided to switch it on and give a little review.

For those that may not know, Aphex Twin, aka Richard D. James, is a name synonymous with the intriguingly named genre of IDM (Intelligent Dance Music). James on the label: “I just think it’s really funny to have terms like that. It’s basically saying ‘this is intelligent and everything else is stupid.’ It’s really nasty to everyone else’s music. It makes me laugh.”

Aphex Twin’s formed Rephlex Records in 1991, releasing three Analogue Bubblebath EPs under the AFX name. He moved to London in 1993, where he released a slew of albums and EPs on Warp Records and other labels under many aliases. His first album ‘Selected Ambient Works 85-92’ was an ambient affair, released in 1992 on R&S records.

In 1995 James began composing on computers, embracing a more drum n bass sound mixed with acid lines. In the late 1990s, his music become more popular with the release of “Come to Daddy” and “Windowlicker”, which James followed this up in 2001 with “Drukqs”, a 2-CD album which featured both prepared piano songs and abrasive and fast drum n bass influenced fare.

In late 2004, James returned to acid techno with the Analord series, which was written and recorded on analogue equipment and pressed to vinyl.

Some seemingly outlandish claims from interviews have been verified. James does own a tank (actually a 1950s armoured scout car, the Daimler Ferret Mark 3) and a submarine bought from Russia.

Additional unverified claims include the following: He composed ambient techno at age 13; he has “over 100 hours” of unreleased music; he experiences synaesthesia; and he is able to incorporate lucid dreaming into the process of making music.

Of course, how could we not add in this seminal classic – Come to Daddy (Directors Cut)

The post WATCH: Take a look at Aphex Twin’s Theis Modular Synthesizer appeared first on Decoded Magazine.

Crow Hill Company creates VAULTS – ACID SYNTH as an homage to the revered Roland TB-303

The Crow Hill Company has announced the availability of VAULTS – ACID SYNTHas the latest entry into its lengthening line of free and accessible virtual instruments arising from company co-founder and composer Christian Henson effectively opening his ‘vaults’ for everyone to enjoy — this time creating an homage to the revered Roland TB-303 Bass Line, launched with high hopes in 1981 as a so-called Computer Controlled bass synth by the Japanese giant of electronic musical instrument manufacturing responsible for its creation before being deemed a commercial failure and discontinued quite quickly thereafter, though it was later instrumental in driving Acid House into the musical mainstream as a whole new EDM (Electronic Dance Music) genre and associated cultural movement, the popularity of which triggered a dramatic rise in the price of used units — as of March 19…

It is fair to say that when Roland released its TB-303 Bass Line in 1981 as a so-called Computer Controlled bass synth with the intention of mimicking the characteristics of an electric bass guitar, there were far fewer tools to create electronic music available. But buyers of the TB-303 Bass Line were possibly swayed by the fact that it could be time-synced — using Roland’s proprietary five-pin DIN sync interface (later superseded by MIDI) — to its contemporarily-released Computer Controlled sibling, the TR-606 Drumatix drum machine, making for an affordable rhythm section that could conceivably fit into a small briefcase as a beautiful backing combination for the solo gigging musician, further helped by both devices being battery operated as an attractive alternative to their (included) AC adapters… or so Roland had hoped.

Though the engineers involved tried their best to imitate the sound of an electric bass guitar with the technology of the time, the TB-303 Bass Line fell short in capturing its subtleties as an instrument, sadly, so it was discontinued within a couple of years, with Roland cheaply selling off the last of the 10,000 units manufactured. Now, normally this would be the end of the story, yet something incredible happened…

Helpfully, for the benefit of anyone not already in the know, The Crow Hill Company’s Theo Le Derf picks up what is an incredible story in itself: “DJ Pierre and his band called Phuture found a used TB-303 in a music shop in Chicago for a bargain price. They started experimenting with the bass sequencer and a drum machine while playing about randomly with the filter and resonance knobs. The sound they produced was so

unique and, frankly, weird that they decided to commit the jam session to tape. With the release of this experiment on Trax Records in 1987,they unwittingly birthed a new genre: Acid House. This slimy, hypnotic, subversive sound subsequently built a cultural movement, and was the soundtrack to many illegal warehouse parties of the late Eighties — all of this from a tiny synth that was used ‘incorrectly’.”

Thanks to VAULTS – ACID SYNTH’s GUI (Graphical User Interface) being as intuitive as always, correct — or ‘incorrect’ — usage of the virtual instrument in question comes quickly. “The first large dial — CUT OFF — is a 24dB lowpass filter that controls what high frequencies you cut out, and it is automatically assigned to MIDI CC1.” So starts Theo Le Derf by way of an appropriately quick guided tour, before continuing:

“RESONANCE determines the peak of the filter as it opens and closes, which creates that characteristically ‘squelchy’ sound that the synth is known for. The small dials are more concentrated effects — MOD controls how much voltage is being sent to the filter, so you can get some really interesting sounds by adjusting the CUT OFF dial and MOD simultaneously; DECAY controls the decay for all the envelopes; and, ofcourse, there is also our standard ECHO and SPLOSH, with the SPLOSH being an algorithmic imitation of a cave, which suits this synth so brilliantly.”

It is as easy as that, though Theo Le Derf is keen to add: “Another thing to mention is that you can obviously play staccato, but when you playlegato — MIDI notes overlapping with one another, you get a glide between the notes. This glide effect is another characteristic that the ’303 is renowned for.”

No need, necessarily, then, to risk dropping a bank-balance-busting four-figure sum on a decades-old Roland TB-303 Bass Line when The CrowHill Company’s ‘tribute’ truly captures the essence of its distinctive sound for all to enjoy for literally nothing. Notes Theo Le Derf, ending on a high note: “The story arc of this synth is so amazing — from its unpromising beginning to completely defining a genre and an era, it’s so exciting to have this celebrated instrument at my fingertips for free!”

VAULTS – ACID SYNTH is free for everyone — as are all VAULTS… releases from The Crow Hill Company — from here: https://thecrowhillcompany.com/vaults/

VAULTS – ACID SYNTH installation and activation requires installation of The Crow Hill App — an easy-to-use app designed by the best in the business to provide seamless download, installation, integration, calibration, and organisation of The Crow Hill Company tools — available for free from here: https://thecrowhillcompany.com/crow-hill-app/

The post Crow Hill Company creates VAULTS – ACID SYNTH as an homage to the revered Roland TB-303 appeared first on Decoded Magazine.

When Ikea met Harry.

Imagine a world where your livelihood requires boundless amounts of objects, all of which must be logged and remembered and stored as a collective body which must be accessible yet compact. These are the problems posed by those vinyl manipulators we know and love – the world of the DJ.

For Harry Love, his problems are long gone. Thanks to our favourite Swedish chaos solution, Ikea, Harry Love can now nonchalantly breeze through his catalogue of keyboards, mixing equipment and 4500 records, following an intervention by the furniture giant which de-cluttered the hip-hop producer’s operational quarters. 

The brand is tapping into audiences by encouraging them to be inventive with Ikea products – why not use a stack of shoe racks to store six keyboards? With the ‘KALLAX’ bookcase fitting conventional vinyl sleeves like a glove, Ikea are reminding the established DJ that their ever-growing collections are screaming for more regimented square storage cases.

The post When Ikea met Harry. appeared first on Decoded Magazine.